Pet Finder using Machine Learning and Deep Learning

¶

Prabal Ghosh

¶

MSc Data Science & Artificial Intelligence

More on Learning Algorithms | Advanced Deep Learning

¶

15/03/2024

¶

This is a Classification task

Introduction¶

The dataset presents pet's characteristics and includes tabular, text and image data. The aim is to predict the rate at which a pet is adopted.

Data fields:

Type - Dog or Cat
Age - Age of pet when listed, in months
Gender - Gender of pet (Male, Female, Mixed, if profile represents group of pets)
Color1 - Color 1 of pet
Color2 - Color 2 of pet
Color3 - Color 3 of pet
MaturitySize - Size at maturity (Small, Medium, Large, Extra Large, Not Specified)
FurLength - Fur length (Short, Medium, Long, Not Specified)
Vaccinated - Pet has been vaccinated (Yes, No, Not Sure)
Dewormed - Pet has been dewormed (Yes, No, Not Sure)
Sterilized - Pet has been spayed / neutered (Yes, No, Not Sure)
Health - Health Condition (Healthy, Minor Injury, Serious Injury, Not Specified)
Fee - Adoption fee (0 = Free)
Breed - breed of pet (see on the dataset)
Description - Profile write-up for this pet. The primary language used is English, with some in Malay or Chinese.
Image - a pointer to an image

The aim is to predic AdoptionSpeed. The value is determined by how quickly, if at all, a pet is adopted. The values are determined in the following way:

0 - Pet was adopted on the same day as it was listed.
1 - Pet was adopted between 1 and 7 days (1st week) after being listed.
2 - Pet was adopted between 8 and 30 days (1st month) after being listed.
3 - Pet was adopted between 31 and 90 days (2nd & 3rd month) after being listed.
4 - No adoption after 100 days of being listed. (There are no pets in this dataset that waited between 90 and 100 days).

Submissions are scored based on the quadratic weighted kappa, which measures the agreement between two ratings. This metrics exist on sklean: sklearn.metrics.cohen_kappa_score with weights="quadratic".

References for this project¶

  • Pipeline- https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
  • KNN- https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
  • RandomOverSampler- https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.RandomOverSampler.html
  • RandomizedSearchCV- https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
  • https://scikit-learn.org/stable/modules/compose.html
  • https://towardsdatascience.com/pipelines-custom-transformers-in-scikit-learn-the-step-by-step-guide-with-python-code-4a7d9b068156
  • https://towardsdatascience.com/customizing-scikit-learn-pipelines-write-your-own-transformer-fdaaefc5e5d7
  • https://www.youtube.com/watch?v=aijB8qbEOQ4
  • keras, sklearn,
  • https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cohen_kappa_score.html

Libraries¶

In [1]:
# Importing required libraries and methods

from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sklearn.compose import make_column_selector as selector
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.compose import ColumnTransformer
from sklearn import linear_model
from sklearn.linear_model import ElasticNet
from sklearn import svm
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.pipeline import make_pipeline
from sklearn import set_config
from sklearn.metrics import mean_squared_error
import seaborn as sns
import matplotlib.pyplot as plt

import nltk

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.util import ngrams
from nltk.tokenize import WhitespaceTokenizer
from nltk.stem import WordNetLemmatizer, PorterStemmer
from nltk.stem.porter import PorterStemmer
from imblearn.over_sampling import SMOTE

from nltk.stem import WordNetLemmatizer, PorterStemmer
import re

from sklearn.feature_extraction.text import CountVectorizer
from imblearn.pipeline import Pipeline as ImbPipeline
from imblearn.over_sampling import SMOTE

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier

import unicodedata

import cv2
from sklearn.cluster import MiniBatchKMeans

#packages
#basics
import os


#modeling
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.regularizers import l2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.client import device_lib

#visualizations
import seaborn as sns
import matplotlib.pyplot as plt
# Import necessary  Libraries
import os
from tqdm import tqdm

import warnings
warnings.filterwarnings("ignore")

import ssl
ssl._create_default_https_context = ssl._create_unverified_context


import numpy as np 
import pandas as pd 

import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
# pip install opencv

Dataset Train and Test¶

In [3]:
# Read train files
df_train = pd.read_csv("C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train.csv")
print("In the dataset they are : ", df_train.shape[0], "train observations")
# df_train.head(1)
In the dataset they are :  9000 train observations
In [4]:
# Read test file
df_test = pd.read_csv("C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test.csv")
print("In the dataset they are : ", df_test.shape[0], "test observations")
# df_test.head(1)
In the dataset they are :  500 test observations
In [5]:
df_train_copy = df_train.copy()
df_train_copy.head(1)
Out[5]:
Type Age Gender Color1 Color2 Color3 MaturitySize FurLength Vaccinated Dewormed Sterilized Health Fee Description AdoptionSpeed Images Breed
0 Dog 84.0 Male Brown Cream Unknown Small No Unknown Yes No Healthy 0.0 He is either lost or abandoned. Please contact... 4.0 3b178aa59-5.jpg Terrier
In [6]:
df_test_copy = df_test.copy()
df_test_copy.head(1)
Out[6]:
Type Age Gender Color1 Color2 Color3 MaturitySize FurLength Vaccinated Dewormed Sterilized Health Fee Description Images Breed
0 Cat 1.0 Male Black White Unknown Small Yes No No No Healthy 0.0 kitten for adoption, pls call for enquiry, off... 5df99d229-2.jpg Domestic_Short_Hair

image path add for train and test data¶

In [7]:
img_dir = "C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train_images_all\\"
test_img_dir = "C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\"
In [8]:
df_train_copy['Images'] = [img_dir+img for img in df_train_copy['Images']]
In [9]:
df_train_copy['Images'][0]
Out[9]:
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train_images_all\\3b178aa59-5.jpg'
In [10]:
df_test_copy['Images'] = [test_img_dir+img for img in df_test_copy['Images']]
In [11]:
df_test_copy['Images'][0]
Out[11]:
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\5df99d229-2.jpg'
In [12]:
df_train_copy.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9000 entries, 0 to 8999
Data columns (total 17 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Type           9000 non-null   object 
 1   Age            9000 non-null   float64
 2   Gender         9000 non-null   object 
 3   Color1         9000 non-null   object 
 4   Color2         9000 non-null   object 
 5   Color3         9000 non-null   object 
 6   MaturitySize   9000 non-null   object 
 7   FurLength      9000 non-null   object 
 8   Vaccinated     9000 non-null   object 
 9   Dewormed       9000 non-null   object 
 10  Sterilized     9000 non-null   object 
 11  Health         9000 non-null   object 
 12  Fee            9000 non-null   float64
 13  Description    9000 non-null   object 
 14  AdoptionSpeed  9000 non-null   float64
 15  Images         9000 non-null   object 
 16  Breed          9000 non-null   object 
dtypes: float64(3), object(14)
memory usage: 1.2+ MB
In [13]:
df_test_copy.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 500 entries, 0 to 499
Data columns (total 16 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Type          500 non-null    object 
 1   Age           500 non-null    float64
 2   Gender        500 non-null    object 
 3   Color1        500 non-null    object 
 4   Color2        500 non-null    object 
 5   Color3        500 non-null    object 
 6   MaturitySize  500 non-null    object 
 7   FurLength     500 non-null    object 
 8   Vaccinated    500 non-null    object 
 9   Dewormed      500 non-null    object 
 10  Sterilized    500 non-null    object 
 11  Health        500 non-null    object 
 12  Fee           500 non-null    float64
 13  Description   500 non-null    object 
 14  Images        500 non-null    object 
 15  Breed         500 non-null    object 
dtypes: float64(2), object(14)
memory usage: 62.6+ KB

Conclusion:

  • No NAN value present.
In [14]:
ordered_series = df_train_copy['AdoptionSpeed'].value_counts().sort_index()
print(ordered_series)
AdoptionSpeed
0.0     247
1.0    1894
2.0    2504
3.0    2061
4.0    2294
Name: count, dtype: int64

Data Visualization¶

Train data¶

Color3 Feature has 7055 Unknown values so drop this cloum¶

In [15]:
df_train_copy.Color3.value_counts()
Out[15]:
Color3
Unknown    7055
White      1451
Cream       186
Gray        146
Yellow       88
Golden       74
Name: count, dtype: int64

Color3 will be dropped as 7055 data values are unknown out of 9000

Type and AdoptionSpeed¶

In [16]:
# display species graph
plt.subplot(1, 2, 1)
df_train_copy['Type'].value_counts().plot(kind='bar', figsize=(12,5),color=['blue','green'])

plt.title('Species')
plt.xlabel('Count')

# display adoption speed graph
plt.subplot(1, 2, 2)
df_train_copy['AdoptionSpeed'].value_counts().sort_index().plot(kind='bar',color = ["red","yellow",'blue','green','orange'])
plt.title('Adoption Speed')
plt.xlabel('Count')
print(df_train_copy['AdoptionSpeed'].value_counts())

# minimize overlap
plt.tight_layout()
plt.show()
AdoptionSpeed
2.0    2504
4.0    2294
3.0    2061
1.0    1894
0.0     247
Name: count, dtype: int64
In [17]:
#https://towardsdatascience.com/python-plotting-basics-simple-charts-with-matplotlib-seaborn-and-plotly-e36346952a3a
fig, ax = plt.subplots(figsize=(3,4))
plt.rcParams['font.size']=17
#percent count
labels = ["dog","cat"]
percentages = [(df_train_copy[df_train_copy["Type"]=="Dog"].AdoptionSpeed.shape[0]*100)/df_train_copy.shape[0],
               (df_train_copy[df_train_copy["Type"]=="Cat"].AdoptionSpeed.shape[0]*100)/df_train_copy.shape[0]]

ax.pie(percentages, labels=labels,  
        autopct='%1.0f%%', 
       shadow=False, startangle=0,   
       pctdistance=0.6,labeldistance=0.8)
ax.axis('equal')
ax.set_title("Cat and Dog distribution")
ax.legend(frameon=False, bbox_to_anchor=(1.5,0.8))
plt.show()

The number of dog pet is higher than the number of cat in the training dataset

AdoptionSpeed ( Target Unbalanced)¶

In [18]:
# Count observations per class
class_counts = df_train_copy['AdoptionSpeed'].value_counts()

# Display results
print(class_counts)
AdoptionSpeed
2.0    2504
4.0    2294
3.0    2061
1.0    1894
0.0     247
Name: count, dtype: int64
In [19]:
#https://towardsdatascience.com/python-plotting-basics-simple-charts-with-matplotlib-seaborn-and-plotly-e36346952a3a
fig, ax = plt.subplots(figsize=(8,4))
plt.rcParams['font.size']=17
#percent count
labels = ["0","1", "2", "3", "4"]
percentages = [(df_train_copy["AdoptionSpeed"].value_counts()[0]*100)/df_train_copy.shape[0],
               (df_train_copy["AdoptionSpeed"].value_counts()[1]*100)/df_train_copy.shape[0],
              (df_train_copy["AdoptionSpeed"].value_counts()[2]*100)/df_train_copy.shape[0],
              (df_train_copy["AdoptionSpeed"].value_counts()[3]*100)/df_train_copy.shape[0],
              (df_train_copy["AdoptionSpeed"].value_counts()[4]*100)/df_train_copy.shape[0]]

ax.pie(percentages, labels=labels,  
        autopct='%1.0f%%', 
       shadow=False, startangle=0,   
       pctdistance=1.2,labeldistance=0.8)
ax.axis('equal')
ax.set_title("AdoptionSpeed class distribution")
ax.legend(frameon=False, bbox_to_anchor=(1.5,0.8))
plt.show()

Conclusion:

  • From above distribution plot it is clear that data is imbalanced and there is only one class that is extreme minor i.e, 0 which counts for only 3% of all the data which is very very small. Rest of other 4 classes have very similar distribution. So we'll have have to take care of minor class.

Our dataset is imbalanced, let's handle this problem later

In [20]:
adoption_speed = df_train_copy['AdoptionSpeed'].value_counts()

plt.figure(figsize=(12, 5))
sns.countplot(x='AdoptionSpeed', hue='Type', data=df_train_copy,palette = "Set2")

plt.title('Distribution of Adoption Speed for Different Types')
plt.xlabel('Adoption Speed')
plt.ylabel('Count')
plt.show()

This shows that most pet were most not likely adopted the same day they were listed and dogs are more likely to get adopted than cats

Pet Age feature¶

In [21]:
sns.barplot(x='AdoptionSpeed', y='Age', data=df_train_copy, estimator='mean')
plt.xlabel('Adoption Speed')
plt.ylabel('Average Age')
plt.title('Average Age vs. Adoption Speed')
plt.show()
In [22]:
print(len(df_train_copy.Age.value_counts()))
df_train_copy.Age.describe()
99
Out[22]:
count    9000.000000
mean       11.809778
std        19.405099
min         0.000000
25%         2.000000
50%         4.000000
75%        12.000000
max       255.000000
Name: Age, dtype: float64
In [ ]:
 
In [23]:
import seaborn as sns
import matplotlib.pyplot as plt

fig, axe = plt.subplots()
count_value = df_train_copy["Age"].value_counts()
sns.set_style('darkgrid')
sns.barplot(x=count_value.values[0:10], y=count_value.index[0:10], orient="h", ax=axe)
plt.xlabel("Count")
plt.ylabel("Age")
plt.show()
  • From above we can take a conclusion that around 75% of pets are between 1 to 12 months old only.
  • It's a very commonsensical that age of pet is very important factor while adoption. So now we'll look at adoption speed with respect to age.

Pet Gender Feature¶

In [24]:
df_train_copy["Gender"].unique()
Out[24]:
array(['Male', 'Female'], dtype=object)
In [25]:
# # gender of pet 

import matplotlib.pyplot as plt

train_dog = df_train_copy[df_train_copy['Type'] == "Dog"]
train_cat = df_train_copy[df_train_copy['Type'] == "Cat"]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7)) 

plt.title("Genders of cats and dogs in train dataset")

train_dog['Gender'].value_counts().plot(kind='pie', figsize=(9, 9), ax=ax1, colors=['yellow', 'green'], autopct='%1.1f%%')
train_cat['Gender'].value_counts().plot(kind='pie', figsize=(9, 9), ax=ax2, colors=['yellow', 'green'], autopct='%1.1f%%')

ax1.set_xlabel('DOGS')
ax2.set_xlabel('CATS')

plt.show()

The gender distribution in test data is not bad. Females are greater in cats and dogs however the difference is not huge.

Breed Feature¶

In [26]:
# df_train_copy['Breed'].unique()
In [27]:
df_train_copy['Breed'].value_counts().head(10)
Out[27]:
Breed
Mixed_Breed             3776
Domestic_Short_Hair     1866
Domestic_Medium_Hair     657
Tabby                    204
Siamese                  170
Persian                  144
Domestic_Long_Hair       143
Shih_Tzu                 138
Labrador_Retriever       138
Poodle                   130
Name: count, dtype: int64
In [ ]:
 
In [28]:
#train.groupby('Health')['Type'].value_counts().plot(kind='barh')

fig, (ax1,ax2) = plt.subplots(1,2, figsize=(10,5))  # 1 row, 2 columns
plt.title("healf by adoption speed  of  cats and dogs in TRAINING DATASET")
train_dog.groupby('Health')['AdoptionSpeed'].value_counts().plot(kind='bar',ax=ax1)
train_cat.groupby('Health')['AdoptionSpeed'].value_counts().plot(kind='bar',ax=ax2)
ax1.set_xlabel('DOGS')
ax2.set_xlabel('CATS')
Out[28]:
Text(0.5, 0, 'CATS')

Heathy animals are most likely to get adopted

Fee Feature¶

In [29]:
df_train_copy.Fee.describe()
Out[29]:
count    9000.000000
mean       24.431333
std        81.575346
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max      2000.000000
Name: Fee, dtype: float64
  • Around 85% of total adoption posting have zero Fee.
  • And there seems some outlier because max fee value is 2000 and that seems alot. We'll do some analysis on fee values.
In [30]:
index = np.argsort(list(df_train_copy.Fee.value_counts().index))
#plotting
plt.figure(figsize=(10,5))
plt.plot(df_train_copy.Fee.value_counts().index[index],
        np.cumsum(df_train_copy.Fee.value_counts().values[index])/np.sum(df_train_copy.Fee.value_counts().values[index]))
plt.title("Adoption Fee")
plt.ylabel("% of total counts")
plt.xlabel("Adoption Fee")
plt.show()
  • So from above it is clear that 99.99% of adoption have Fee below 1000 so we can discard rest of entries with Fee above 1000.

Separating Numerical and Categorical columns¶

I am going to separate the numerical and categorical in different lists because later I would need to apply different trasformations to each type of column in the pipeline. I am not goind to take into account the target, description and images columns because they are not numerical or categorical (technically, the target is categorical but it does not matter as we want to predict it and will not use it as part of our input data).

I do not want ot change the original dataset so I will assign X (input data) and y (target) to different variables.

In [31]:
X = df_train_copy.drop('AdoptionSpeed', axis=1).copy()
y = np.array(df_train_copy['AdoptionSpeed']).reshape(-1,1)
In [32]:
numerical_columns_selector = selector(dtype_exclude=object) 
categorical_columns_selector = selector(pattern=r'^(?!.*(Description|Images))',dtype_include=object)
numerical_columns = numerical_columns_selector(X)
categorical_columns = categorical_columns_selector(X)
In [33]:
print("List of numerical columns: ",numerical_columns)
print("List of categorical columns: ", categorical_columns)
List of numerical columns:  ['Age', 'Fee']
List of categorical columns:  ['Type', 'Gender', 'Color1', 'Color2', 'Color3', 'MaturitySize', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']

Type is transformed converted it into cat = 0 and dog=1 (Custom pipeline function)¶

In [34]:
from sklearn.base import BaseEstimator, TransformerMixin

class TypeConverter(BaseEstimator, TransformerMixin):
    def __init__(self):
        pass
    
    def fit(self, X, y=None):
        return self
    
    def transform(self, X):
        X_transformed = X.copy()
        X_transformed['Type'] = X_transformed['Type'].map({'Dog': 1, 'Cat': 0})
        return X_transformed
In [35]:
# # Create the custom pipeline
# type_pipeline = Pipeline([
#     ('type_converter', TypeConverter())
# ])

# # Example usage:
# # Assuming df is your DataFrame with 'Gender' column
# transformed_df = type_pipeline.fit(X)
In [36]:
# type_pipeline.transform(X)

Age is transformed (Custom pipeline function)¶

Dogs age at a rate of one-fifth that of humans, while cats age at a rate of one-seventh that of humans. I applied this approach to make their ages comparable to those of humans

  • https://www.quora.com/Why-are-cat-years-different-to-human-years
In [37]:
from sklearn.base import BaseEstimator, TransformerMixin

class AgeConverter(BaseEstimator, TransformerMixin):
    def __init__(self, cat_scale=5, dog_scale=7):
        
        self.cat_scale = cat_scale
        self.dog_scale = dog_scale
    
    def fit(self, X, y=None):

        return self
    
    def transform(self, X):
        
        X_transformed = X.copy()
        
        # Apply the age conversion for cats
        X_transformed.loc[X_transformed['Type'] == 'Cat', 'Age'] = \
            X_transformed.loc[X_transformed['Type'] == 'Cat', 'Age'] / self.cat_scale
        
        # Apply the age conversion for dogs
        X_transformed.loc[X_transformed['Type'] == 'Dog', 'Age'] = \
            X_transformed.loc[X_transformed['Type'] == 'Dog', 'Age'] / self.dog_scale
        
        return X_transformed
In [ ]:
 

Gender = Male is converted to 0 and Female is converted to 1 (Custom pipeline function)¶

In [38]:
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
import pandas as pd

class GenderConverter(BaseEstimator, TransformerMixin):
    def __init__(self):
        pass
    
    def fit(self, X, y=None):
        return self
    
    def transform(self, X):
        X_transformed = X.copy()
        X_transformed['Gender'] = X_transformed['Gender'].map({'Male': 1, 'Female': 0})
        return X_transformed
In [39]:
# # Create the custom pipeline
# gender_pipeline = Pipeline([
#     ('gender_converter', GenderConverter())
# ])

# # Example usage:
# # Assuming df is your DataFrame with 'Gender' column
# transformed_df = gender_pipeline.transform(X)

Ordinal Mapping (MaturitySize)¶

Ordinal encoding, is a process of transforming categorical variables into numerical variables while preserving the order or hierarchy of the categories. We can apply it to columns of our dataset which are categorical at a first glance but which some kind of progression can be identified in the unique values of the column (such as a progression in size, age, etc).

To identify this, we need to study our columns' unique values.

The column MaturitySize is ordinal (there is a progression from small to extra large in its values), so we can map it as an ordinal column.

MaturitySize is converted into numerical (Custom pipeline function)¶

In [40]:
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
import pandas as pd

class MaturitySizeConverter(BaseEstimator, TransformerMixin):
    def __init__(self):
        pass
    
    def fit(self, X, y=None):
        return self
    
    def transform(self, X):
        X_transformed = X.copy()
        size_mapping = {'Small': 0, 'Medium': 1, 'Large': 2, 'Extra Large': 3}
        X_transformed['MaturitySize'] = X_transformed['MaturitySize'].map(size_mapping)
        return X_transformed

# Create the custom pipeline
maturity_size_pipeline = Pipeline([
    ('maturity_size_converter', MaturitySizeConverter())
])

Text pre-processing¶

In [41]:
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score, classification_report
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from sklearn.pipeline import make_pipeline
import nltk
import emoji
import string
# from spellchecker import SpellChecker
# from textblob import TextBlob
from nltk.stem import WordNetLemmatizer
In [42]:
# nltk.download('stopwords')
# nltk.download('punkt')
# nltk.download('wordnet')
# nltk.download('omw-1.4')
# stops = set(stopwords.words('english'))
In [43]:
# emoji handling
# !pip install emoji
import re

clean description using TextCleaner (Custom pipeline function)¶

In [44]:
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import re
import pandas as pd

class TextCleaner(BaseEstimator, TransformerMixin):
    def __init__(self):
        pass
    
    def fit(self, X, y=None):
        return self
    
    def transform(self, X):
        X_transformed = X.copy()
        X_transformed['Description'] = X_transformed['Description'].apply(self.clean_text1)
        return X_transformed
    
    def clean_text1(self, text):
        stop_words = set(stopwords.words('english'))
        
        # Initialize the WordNetLemmatizer
        lemmatizer = WordNetLemmatizer()
        
        # Remove emojis
        emoji_pattern = re.compile("["
                                    u"\U0001F600-\U0001F64F"  # emoticons
                                    u"\U0001F300-\U0001F5FF"  # symbols & pictographs
                                    u"\U0001F680-\U0001F6FF"  # transport & map symbols
                                    u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
                                    u"\U0001F700-\U0001F77F"  # alchemical symbols
                                    u"\U0001F780-\U0001F7FF"  # Geometric Shapes Extended
                                    u"\U0001F800-\U0001F8FF"  # Supplemental Arrows-C
                                    u"\U0001F900-\U0001F9FF"  # Supplemental Symbols and Pictographs
                                    u"\U0001FA00-\U0001FA6F"  # Chess Symbols
                                    u"\U0001FA70-\U0001FAFF"  # Symbols and Pictographs Extended-A
                                    u"\U00002702-\U000027B0"  # Dingbats
                                    u"\U000024C2-\U0001F251" 
                                    u"\U0001F910-\U0001F97A"
                                    u"\U0001F980-\U0001F9B0"
                                    u"\U0001F9C0-\U0001F9FF"
                                    u"\U0001FA60-\U0001FA6F"
                                    u"\U0001FA70-\U0001FAFF"
                                    u"\U00002600-\U000026FF"  # Miscellaneous Symbols
                                    u"\U00002700-\U000027BF"  # Dingbats
                                    u"\U00002B50"
                                    u"\U000023E9-\U000023FA"  # Miscellaneous Technical
                                    "]+", flags=re.UNICODE)
        text = emoji_pattern.sub(r'', text)

        # Tokenize the text
        text_tokens = word_tokenize(text)
        
        # Lemmatize the tokens and remove stop words
        lemmatized_text = [lemmatizer.lemmatize(word.lower()) for word in text_tokens if word.lower() not in stop_words]
        
        # Join the lemmatized tokens back into a sentence
        cleaned_sentence = ' '.join(lemmatized_text)
        
        # Remove punctuation and extra whitespace
        cleaned_sentence = re.sub(r'[^\w\s]', '', cleaned_sentence).strip()
        
        return cleaned_sentence

IMAGE PRE-PROCESSING¶

In [45]:
# Read the first image of the list
from skimage import io


img = io.imread(X['Images'][10])
# have a look to the image
plt.imshow(img)
Out[45]:
<matplotlib.image.AxesImage at 0x112fb1a0650>
In [46]:
# !pip install opencv-python
# !pip install opencv-contrib-python
In [47]:
# convert the image to grey levels 
import cv2

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
In [48]:
# compute SIFT detector and descriptors
sift = cv2.SIFT_create()
kp,des = sift.detectAndCompute(gray,None)
In [49]:
# plot image and descriptors
cv2.drawKeypoints(img,kp,img,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
plt.imshow(img)
Out[49]:
<matplotlib.image.AxesImage at 0x112f6b23150>

Extract features and build BOFs¶

In [50]:
from sklearn.base import BaseEstimator,TransformerMixin
In [ ]:
 
In [51]:
import cv2
from sklearn.cluster import MiniBatchKMeans

SIFT (Scale-Invariant Feature Transform) is a feature extraction technique used in computer vision and image processing for detecting and describing local features in images. SIFT features are useful for a wide range of applications such as object recognition, image registration, 3D reconstruction, and image retrieval.

This code performs the following steps:

  • Extracts Scale-Invariant Feature Transform (SIFT) features from a list of images and returns a list of these features.

  • Builds a clusterizer (a clustering algorithm) using the list of extracted SIFT features and a desired number of clusters. The clusterizer is then fitted to the SIFT features and returned.

  • Constructs a Bag of Features (BOF) representation using the list of SIFT features and the fitted clusterizer. The BOF representation is a histogram of the frequency of each cluster (i.e., visual word) in the SIFT features of an image.

In [52]:
import cv2

def extract_SIFT(img_lst):
    sift = sift = cv2.SIFT_create()
    sift_lst = []
    for img in img_lst:
        img=cv2.imread(img) #Load images
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #Convert to gray scale
        kp, des = sift.detectAndCompute(gray, None) #Extract SIFT (Scale-Invariant Feature Transform) keypoints and descriptors
        sift_lst.append(des)
    
    return sift_lst
In [53]:
from sklearn.cluster import KMeans

def clusterize(SIFs, nb_cluster):
    sift_features = np.vstack(SIFs)#Stack the SIFT features
    clusterizer = KMeans(n_clusters=nb_cluster)# Create the KMeans clusterizer
    clusterizer.fit(sift_features)# Fit the clusterizer with the SIFT features
    return clusterizer
In [54]:
def build_BOFs(SIFTs, clusterizer):
    #Initialize the BOF representation
    bof_representation = np.zeros((len(SIFTs), clusterizer.n_clusters), dtype=np.float32)
    
    #Loop through the SIFT features
    for i, sift in enumerate(SIFTs):
        cluster_labels = clusterizer.predict(sift) #Predict the cluster labels for the SIFT features
        histogram = np.bincount(cluster_labels, minlength=clusterizer.n_clusters) #Create a histogram of cluster labels
        histogram = histogram / histogram.sum() #Normalize the histogram
        bof_representation[i, :] = histogram #Store the histogram in the BOF representation
    
    return bof_representation
In [55]:
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin


class BOF_extractor(BaseEstimator,TransformerMixin): # Initialise class MyImageTransformer and nb of clusters
    def __init__(self, nb_cluster=4):
        self.nb_cluster=4

    def fit(self, X, y=None): #Use function extract_SIFT(X) and clusterize(SIFTs, self.nb_cluster) to the input data
        self.SIFTs = extract_SIFT(X)
        self.clusterizer = clusterize(SIFTs, self.nb_cluster)
        
    def transform(self, X, y=None): #transform input images and return bof representation using build_BOFs()
        self.SIFTs = extract_SIFT(X)
        return build_BOFs(self.SIFTs, self.clusterizer)
    
    def fit_transform(self, X, y=None): #transform and fit the data using all the previously defined function
        self.SIFTs = extract_SIFT(X)
        self.clusterizer = clusterize(self.SIFTs, self.nb_cluster)
        return build_BOFs(self.SIFTs, self.clusterizer)
    
In [ ]:
 

Pre-processing Pipeline Create X and y(Target) and Split the data into train and validation set¶

To achieve accurate machine learning results, it's important to use a balanced dataset. This means that the number of samples for each class in the dataset should be roughly the same. If the dataset is imbalanced, the machine learning model may be biased towards the majority class, leading to poor performance on the minority class.

To address this issue, my approach is to balance the dataset using a technique like RandomOverSampler.

  • https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.RandomOverSampler.html

Performing cross-validation is a powerful method to evaluate the performance of a machine learning model. However, it can be computationally expensive, especially when the dataset is large. In this case, In my case I am facing a time constraint, and the computer takes a long time to perform cross-validation.

  • https://scikit-learn.org/stable/modules/cross_validation.html

To optimize my time, I am planning to only perform cross-validation on the balanced dataset since this is the recommended approach. I will not perform cross-validation on the non-balanced dataset because I only want to compare its accuracy with the accuracy of the balanced dataset

In [56]:
#spliting the data into dependent and independent varaible

y = df_train_copy['AdoptionSpeed']
X = df_train_copy.drop(['AdoptionSpeed'], axis=1).copy()
In [57]:
# Split the data into train and validation set 
from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.20, random_state=2022,stratify=y)
In [ ]:
 
In [358]:
# y
In [59]:
numerical_columns
Out[59]:
['Age', 'Fee']
In [60]:
categorical_columns
Out[60]:
['Type',
 'Gender',
 'Color1',
 'Color2',
 'Color3',
 'MaturitySize',
 'FurLength',
 'Vaccinated',
 'Dewormed',
 'Sterilized',
 'Health',
 'Breed']
In [61]:
import pandas as pd

features = X.columns.tolist()
# List comprehension to separate numerical and categorical features
# For numerical features, we include those whose dtype is not 'object',
# and also specifically include 'MaturitySize', 'Gender', and 'Type' columns

numerical_features = [feature for feature in features if 
                X[feature].dtype != 'object' or feature == 'MaturitySize' or feature ==  'Gender' or feature == "Type"]

# For categorical features, we include those whose dtype is 'object' 
# but exclude certain columns like 'MaturitySize', 'Gender', 'Description', 'Images','Color3' , 'Type'.

categorical_features = [feature for feature in features if
                X[feature].dtype == object and feature != "MaturitySize" and  feature != "Gender"   and  feature != "Description"
                        and  feature != "Images"  and  feature != "Color3" and  feature != "Type" ]


print(numerical_features)
# print("\n")

print(categorical_features)
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
In [62]:
text_features = ['Description']
image_features = ['Images']
drop_features = ["Color3"]
In [63]:
#  checking the number of observations per classes
y.value_counts()
Out[63]:
AdoptionSpeed
2.0    2504
4.0    2294
3.0    2061
1.0    1894
0.0     247
Name: count, dtype: int64
In [64]:
#  checking the number of observations per classes
y_train.value_counts()
Out[64]:
AdoptionSpeed
2.0    2003
4.0    1835
3.0    1649
1.0    1515
0.0     198
Name: count, dtype: int64
In [65]:
y_train.shape
Out[65]:
(7200,)

Pipeline¶

  • https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html
In [66]:
from imblearn.pipeline import Pipeline as ImbPipeline
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.utils import resample
In [67]:
# Creating pipelines for numerical and categorical features
numerical_pipeline = Pipeline(steps=[
    ('stdscaler', StandardScaler())
])

categorical_pipeline = Pipeline(steps=[
    ('onehot', OneHotEncoder(handle_unknown='ignore'))  # One-hot encoding categorical features
])

# Define pipeline for text features
text_transformer_pipeline = Pipeline(steps=[ ('vectorizer', CountVectorizer())])
image_transformer_pipeline = Pipeline(steps=[ ('img', BOF_extractor())])



col_transformer = ColumnTransformer(transformers=[
   ('drop_columns','drop',drop_features),

    ('numerical', numerical_pipeline, numerical_features),
    ('categorical', categorical_pipeline, categorical_features),
    
    ('text-data', text_transformer_pipeline, 'Description'),
    ('image-data', image_transformer_pipeline, 'Images'),
    
    
])


    
my_sklearn_pipeline = Pipeline(steps=[
    ('TypeConverter', TypeConverter()),
    ('age', AgeConverter()),
    ('text_cleaner', TextCleaner()),
    ('MaturitySizeConverter' ,MaturitySizeConverter()),
    ("GenderConverter",GenderConverter()),

                 ('col_transformer',col_transformer),
    ]

                           )
In [ ]:
 
In [68]:
my_sklearn_pipeline
Out[68]:
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'A...Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer())]),
                                                  'Description'),
                                                 ('image-data',
                                                  Pipeline(steps=[('img',
                                                                   BOF_extractor())]),
                                                  'Images')]))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'A...Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer())]),
                                                  'Description'),
                                                 ('image-data',
                                                  Pipeline(steps=[('img',
                                                                   BOF_extractor())]),
                                                  'Images')]))])
TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
                                ('numerical',
                                 Pipeline(steps=[('stdscaler',
                                                  StandardScaler())]),
                                 ['Type', 'Age', 'Gender', 'MaturitySize',
                                  'Fee']),
                                ('categorical',
                                 Pipeline(steps=[('onehot',
                                                  OneHotEncoder(handle_unknown='ignore'))]),
                                 ['Color1', 'Color2', 'FurLength', 'Vaccinated',
                                  'Dewormed', 'Sterilized', 'Health',
                                  'Breed']),
                                ('text-data',
                                 Pipeline(steps=[('vectorizer',
                                                  CountVectorizer())]),
                                 'Description'),
                                ('image-data',
                                 Pipeline(steps=[('img', BOF_extractor())]),
                                 'Images')])
['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer()
Images
BOF_extractor()
In [ ]:
 

fit and Transfrom the data using the pipeline so that I can use that for randomsearch and model fitting¶

Pipeline is fitted using all data and stored in a variable for model training it will save time as its very hard to handel these 9000 images in my laptop¶

In [71]:
#  fits the sklearn pipeline `my_sklearn_pipeline` to the training data X_train.

my_sklearn_pipeline.fit(X_train)
Out[71]:
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'A...Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer())]),
                                                  'Description'),
                                                 ('image-data',
                                                  Pipeline(steps=[('img',
                                                                   BOF_extractor())]),
                                                  'Images')]))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'A...Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer())]),
                                                  'Description'),
                                                 ('image-data',
                                                  Pipeline(steps=[('img',
                                                                   BOF_extractor())]),
                                                  'Images')]))])
TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
                                ('numerical',
                                 Pipeline(steps=[('stdscaler',
                                                  StandardScaler())]),
                                 ['Type', 'Age', 'Gender', 'MaturitySize',
                                  'Fee']),
                                ('categorical',
                                 Pipeline(steps=[('onehot',
                                                  OneHotEncoder(handle_unknown='ignore'))]),
                                 ['Color1', 'Color2', 'FurLength', 'Vaccinated',
                                  'Dewormed', 'Sterilized', 'Health',
                                  'Breed']),
                                ('text-data',
                                 Pipeline(steps=[('vectorizer',
                                                  CountVectorizer())]),
                                 'Description'),
                                ('image-data',
                                 Pipeline(steps=[('img', BOF_extractor())]),
                                 'Images')])
['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer()
Images
BOF_extractor()
In [72]:
#  transform the  the training data X_train.

X_train_transformed = my_sklearn_pipeline.transform(X_train)
In [73]:
#  transform the  the validation data X_val.

X_val_transformed = my_sklearn_pipeline.transform(X_val)
In [ ]:
 
In [ ]:
 

RandomOverSampler is used to balance the data by oversampling the minority class¶

RandomOverSampler is used on X_train and y_train to balance the training data.

  • It's important to use resampling techniques like RandomOverSampler on the training data rather than the original data to avoid data leakage. This ensures that the model is trained on balanced data while maintaining the integrity of the validation and test datasets. Data leakage can occur when information from the validation or test sets inadvertently influences the training process, leading to overly optimistic performance estimates. Resampling techniques applied solely to the training data help mitigate this risk by ensuring that the model learns from balanced representations of each class without peeking at unseen data during training.
In [74]:
from imblearn.over_sampling import RandomOverSampler


# desired number of samples for the minority class
desired_minority_samples = 800  
minority_class_label = 0.0


# Create a dictionary with the desired sampling strategy
sampling_strategy = {minority_class_label : desired_minority_samples}

# RandomOverSampler with the specified sampling strategy
ros = RandomOverSampler(sampling_strategy=sampling_strategy)

X_train_transformed_resampled, y_train_resampled = ros.fit_resample(X_train_transformed, y_train)

ax = y_train_resampled.value_counts().plot.pie(autopct='%.2f')
_ = ax.set_title("Over-sampling")
y_train_resampled.value_counts()
Out[74]:
AdoptionSpeed
2.0    2003
4.0    1835
3.0    1649
1.0    1515
0.0     800
Name: count, dtype: int64

I will use this resampled data set for my model training

In [75]:
X_train_transformed_resampled
Out[75]:
<7802x14653 sparse matrix of type '<class 'numpy.float64'>'
	with 361217 stored elements in Compressed Sparse Row format>
In [76]:
X_val_transformed
Out[76]:
<1800x14653 sparse matrix of type '<class 'numpy.float64'>'
	with 83106 stored elements in Compressed Sparse Row format>

X_train_transformed_resampled is converted into a dataframe

In [87]:
import pandas as pd


dense_array_X_train_transformed_resampled = X_train_transformed_resampled.toarray()

df_X_train_transformed_resampled = pd.DataFrame(dense_array)
df_X_train_transformed_resampled.head(2)
Out[87]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 0.101380 1.140316 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.298734 0.124051 0.281013 0.296203
1 -1.226519 1.220209 -0.876950 2.036438 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.471074 0.041322 0.247934 0.239669

2 rows × 14653 columns

target class is marged with the X_train_transformed_resampled dataframe

In [89]:
import pandas as pd

# Create a copy of the DataFrame
new_df_train = df_X_train_transformed_resampled.copy()

# Add the Series as a new column to the new DataFrame
new_df_train['AdoptionSpeed'] = y_train_resampled

new_df_train.head(2)
Out[89]:
0 1 2 3 4 5 6 7 8 9 ... 14644 14645 14646 14647 14648 14649 14650 14651 14652 AdoptionSpeed
0 -1.226519 0.101380 1.140316 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.298734 0.124051 0.281013 0.296203 4.0
1 -1.226519 1.220209 -0.876950 2.036438 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.471074 0.041322 0.247934 0.239669 2.0

2 rows × 14654 columns

X_val_transformed is converted into a dataframe

In [90]:
import pandas as pd
dense_array_val_X_val_transformed = X_val_transformed.toarray()
df_val_X_val_transformed = pd.DataFrame(dense_array_val_X_val_transformed)
df_val_X_val_transformed.head(2)
Out[90]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 -0.559747 1.140316 -1.620921 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.285714 0.069426 0.353805 0.291055
1 0.815316 -0.051188 -0.876950 0.207758 2.196528 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.177449 0.052372 0.411583 0.358595

2 rows × 14653 columns

target class is marged with the X_val_transformed dataframe

In [91]:
import pandas as pd

# Create a copy of the DataFrame
new_df_validation = df_val_X_val_transformed.copy()

# Add the Series as a new column to the new DataFrame
new_df_validation['AdoptionSpeed'] = y_val

new_df_validation.head(2)
Out[91]:
0 1 2 3 4 5 6 7 8 9 ... 14644 14645 14646 14647 14648 14649 14650 14651 14652 AdoptionSpeed
0 -1.226519 -0.559747 1.140316 -1.620921 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.285714 0.069426 0.353805 0.291055 NaN
1 0.815316 -0.051188 -0.876950 0.207758 2.196528 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.177449 0.052372 0.411583 0.358595 3.0

2 rows × 14654 columns

In [191]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
As my laptop is not capable to process this big data so I have stored the transformed X_train and 

save the transformed data as csv so that I can use those during ml and dl training¶

X_train_transformed as df dataframe is stored in X_train_transformed_df.csv

In [221]:
# df_X_train_transformed_resampled.to_csv('X_train_transformed_resampled.csv')
In [74]:
# df_X_train_transformed_resampled = pd.read_csv('X_train_transformed_resampled.csv') 

X_val_transformed as df_val dataframe is stored in X_val_transformed_df.csv

In [222]:
# df_val_X_val_transformed.to_csv('X_val_transformed.csv')
In [75]:
# df_val_X_val_transformed = pd.read_csv('X_val_transformed.csv') 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 

CROSS VALIDATION AND RANDOM SEARCH CV¶

In [ ]:
 

Train test split the balanced dataset with only 400 data for each class from the resampled data¶

In [92]:
unique_classes = new_df_train['AdoptionSpeed'].unique()  # Identify unique classes
selected_df = pd.DataFrame(columns=new_df_train.columns) # empty dataframe to store selected data

# Sample 200 data points from each class
for class_label in unique_classes:
    class_data = new_df_train[new_df_train['AdoptionSpeed'] == class_label]
    if len(class_data) < 400:
        selected_df = pd.concat([selected_df, class_data])
    else:
        sampled_data = class_data.sample(n=400, replace=False, random_state=42)
        selected_df = pd.concat([selected_df, sampled_data])

# Reset index of the selected dataframe
selected_df.reset_index(drop=True, inplace=True)

selected_df.head(2)
Out[92]:
0 1 2 3 4 5 6 7 8 9 ... 14644 14645 14646 14647 14648 14649 14650 14651 14652 AdoptionSpeed
0 0.815316 1.830480 1.140316 -1.620921 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.225670 0.131171 0.377997 0.265162 4.0
1 -1.226519 0.609939 1.140316 0.207758 -0.310264 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.330065 0.163399 0.196078 0.310458 4.0

2 rows × 14654 columns

In [93]:
selected_df['AdoptionSpeed'].value_counts()
Out[93]:
AdoptionSpeed
4.0    400
2.0    400
1.0    400
3.0    400
0.0    400
Name: count, dtype: int64
In [94]:
# Separate features (X_resampled) and target variable (y_resampled)
X_selected_400 = selected_df.drop(columns=['AdoptionSpeed'])
y_selected_400 = selected_df['AdoptionSpeed']
In [ ]:
 

The different models with the best hyper parameters, after the cross validation.¶

The RandomizedSearchCV I have done only with the balanced dataset, I haven't try even with the non balanced dataset because in my computer it takes more than one day and sometimes it froze and I needed to switch off the computer and start again.

Cross-validation with RandomizedSearchCV is a technique used to optimize the hyperparameters of a machine learning model using randomized search. RandomizedSearchCV is a function from the scikit-learn library that performs hyperparameter tuning using a combination of random search and cross-validation.

The process works by defining a range of hyperparameters and their potential values. RandomizedSearchCV then randomly selects a combination of hyperparameter values from this range and trains and evaluates the model using cross-validation. The process is repeated multiple times, each time selecting a different set of hyperparameter values.

The advantage of RandomizedSearchCV is that it can be more efficient than an exhaustive grid search of all possible hyperparameter combinations. By randomly sampling the hyperparameter space, RandomizedSearchCV can often find good hyperparameter values with fewer evaluations.

The output of RandomizedSearchCV is the best set of hyperparameters that was found during the search, along with the corresponding cross-validation score. These hyperparameters can then be used to train the final model on the entire dataset.

Models where I will train with balance dataset (RandomizedSearchCV )¶
In [232]:
from sklearn.model_selection import RandomizedSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression

# hyperparameters
param_distributions = {
    'RandomForestClassifier': {
        # Random Forest hyperparameters
        'randomforestclassifier__n_estimators': [20, 50, 150],  # Number of trees in the forest
        'randomforestclassifier__max_depth': [15, 50, 100],     # Maximum depth of the tree
        'randomforestclassifier__bootstrap': [True, False],     # Whether bootstrap samples are used when building trees
        'randomforestclassifier__min_samples_split': [2, 5, 10], # Minimum number of samples required to split a node
        'randomforestclassifier__min_samples_leaf': [1, 2, 4],  # Minimum number of samples required to be at a leaf node
        'randomforestclassifier__max_features': ['auto', 'sqrt', 'log2']  # Number of features to consider when looking for the best split
    },
    'GradientBoostingClassifier': {
        # Gradient Boosting hyperparameters
        'gradientboostingclassifier__n_estimators': [80, 100, 120],   # Number of boosting stages to be run
        'gradientboostingclassifier__learning_rate': [0.1, 0.2, 0.3],  # Learning rate shrinks the contribution of each tree
    },
    'KNN_CLF': {
        # KNN hyperparameters
        'kneighborsclassifier__n_neighbors': [4, 5, 6],          # Number of neighbors to use
        'kneighborsclassifier__weights': ['uniform', 'distance'],  # Weight function used in prediction
    },
    'LogisticRegression': {
        # Logistic Regression hyperparameters
        'logisticregression__penalty': ['l1', 'l2', 'elasticnet'],  # Regularization penalty
        'logisticregression__C': [0.2, 0.5, 1.0, 2.0,3.0,4.0],       # Inverse of regularization strength
        'logisticregression__verbose': [1, 2, 3],                    # Verbosity mode
        'logisticregression__max_iter': [5000],                     # Maximum number of iterations
        'logisticregression__solver': ['liblinear', 'saga']         # Algorithm to use in the optimization problem
    }
}



# Initialize classifiers
# classifiers = {
#     'GradientBoostingClassifier': make_pipeline(my_sklearn_pipeline, GradientBoostingClassifier()),
#     'KNN_CLF': make_pipeline(my_sklearn_pipeline, KNeighborsClassifier()),
#     'LogisticRegression': make_pipeline(my_sklearn_pipeline, LogisticRegression()),
#     'RandomForestClassifier': make_pipeline(my_sklearn_pipeline, RandomForestClassifier())
# }


classifiers = {
    'GradientBoostingClassifier': make_pipeline( GradientBoostingClassifier()),
    'KNN_CLF': make_pipeline( KNeighborsClassifier()),
    'LogisticRegression': make_pipeline( LogisticRegression()),
    'RandomForestClassifier': make_pipeline( RandomForestClassifier())
}
In [233]:
classifiers
Out[233]:
{'GradientBoostingClassifier': Pipeline(steps=[('gradientboostingclassifier', GradientBoostingClassifier())]),
 'KNN_CLF': Pipeline(steps=[('kneighborsclassifier', KNeighborsClassifier())]),
 'LogisticRegression': Pipeline(steps=[('logisticregression', LogisticRegression())]),
 'RandomForestClassifier': Pipeline(steps=[('randomforestclassifier', RandomForestClassifier())])}
In [235]:
# from sklearn.model_selection import RandomizedSearchCV

# random_search = RandomizedSearchCV(estimator=model, param_distributions=params, n_jobs=-1)
In [237]:
# Perform random search for each algorithm
best_params = {}
for algo, clf in classifiers.items():
    search = RandomizedSearchCV(clf, param_distributions[algo], n_iter=10, cv=3, random_state=42)
    search.fit(X_selected_400, y_selected_400)
    best_params[algo] = search.best_params_

# Print best parameters for each algorithm
for algo, params in best_params.items():
    print(f"Best parameters for {algo}: {params}")
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3448 epochs took 2584 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 43.1min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 43.1min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 4136 epochs took 2975 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 49.6min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 49.6min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3491 epochs took 2550 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 42.5min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 42.5min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 467 epochs took 246 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.1min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.1min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 445 epochs took 231 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  3.9min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  3.9min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 556 epochs took 293 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.9min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.9min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1290 epochs took 839 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 14.0min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 14.0min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 376 epochs took 245 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.1min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.1min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 801 epochs took 592 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  9.9min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  9.9min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1945 epochs took 1221 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 20.3min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 20.3min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1236 epochs took 778 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 13.0min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 13.0min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1969 epochs took 1266 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 21.1min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 21.1min finished
[LibLinear][LibLinear][LibLinear]
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1179 epochs took 781 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 13.0min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 659 epochs took 437 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  7.3min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 1126 epochs took 747 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 12.4min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3466 epochs took 2802 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 46.7min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 46.7min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 4379 epochs took 3672 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 61.2min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 61.2min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 3770 epochs took 3503 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 58.4min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 58.4min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 468 epochs took 313 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  5.2min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  5.2min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 444 epochs took 295 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.9min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  4.9min finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
convergence after 555 epochs took 367 seconds
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  6.1min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  6.1min finished
[LibLinear]Best parameters for GradientBoostingClassifier: {'gradientboostingclassifier__n_estimators': 120, 'gradientboostingclassifier__learning_rate': 0.3}
Best parameters for KNN_CLF: {'kneighborsclassifier__weights': 'distance', 'kneighborsclassifier__n_neighbors': 6}
Best parameters for LogisticRegression: {'logisticregression__verbose': 3, 'logisticregression__solver': 'liblinear', 'logisticregression__penalty': 'l2', 'logisticregression__max_iter': 5000, 'logisticregression__C': 2.0}
Best parameters for RandomForestClassifier: {'randomforestclassifier__n_estimators': 50, 'randomforestclassifier__min_samples_split': 5, 'randomforestclassifier__min_samples_leaf': 1, 'randomforestclassifier__max_features': 'sqrt', 'randomforestclassifier__max_depth': 100, 'randomforestclassifier__bootstrap': True}

Best Parameters

  • Best parameters for GradientBoostingClassifier: {'gradientboostingclassifiern_estimators': 120, 'gradientboostingclassifierlearning_rate': 0.3}

:

  • Best parameters for KNN_CLF: {'kneighborsclassifierweights': 'distance', 'kneighborsclassifiern_neighbors': 6}

:

  • Best parameters for LogisticRegression: {'logisticregressionverbose': 3, 'logisticregressionsolver': 'liblinear', 'logisticregressionpenalty': 'l2', 'logisticregressionmax_iter': 5000, 'logisticregression__C': 2.0}

:

  • Best parameters for RandomForestClassifier: {'randomforestclassifiern_estimators': 50, 'randomforestclassifiermin_samples_split': 5, 'randomforestclassifiermin_samples_leaf': 1, 'randomforestclassifiermax_features': 'sqrt', 'randomforestclassifiermax_depth': 100, 'randomforestclassifierbootstrap': True}
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 

Model Training using the whole Resampled train data¶

In [116]:
df_X_train_transformed_resampled
Out[116]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 0.101380 1.140316 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.298734 0.124051 0.281013 0.296203
1 -1.226519 1.220209 -0.876950 2.036438 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.471074 0.041322 0.247934 0.239669
2 0.815316 -0.559747 -0.876950 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.351990 0.078358 0.268657 0.300995
3 0.815316 -0.559747 -0.876950 0.207758 1.569830 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.438144 0.051546 0.280928 0.229381
4 -1.226519 -0.102044 1.140316 2.036438 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.478992 0.033613 0.142857 0.344538
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7797 -1.226519 -0.152900 -0.876950 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.079652 0.080377 0.495293 0.344678
7798 0.815316 3.559580 1.140316 2.036438 3.449925 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.365782 0.103245 0.300885 0.230089
7799 -1.226519 -0.508891 1.140316 -1.620921 -0.310264 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.323767 0.060987 0.309417 0.305830
7800 -1.226519 -0.458035 -0.876950 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.393939 0.145455 0.230303 0.230303
7801 -1.226519 -0.508891 1.140316 -1.620921 -0.310264 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.323767 0.060987 0.309417 0.305830

7802 rows × 14653 columns

In [117]:
df_val_X_val_transformed
Out[117]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 -0.559747 1.140316 -1.620921 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.285714 0.069426 0.353805 0.291055
1 0.815316 -0.051188 -0.876950 0.207758 2.196528 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.177449 0.052372 0.411583 0.358595
2 0.815316 -0.458035 -0.876950 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.457912 0.097643 0.249158 0.195286
3 0.815316 -0.508891 -0.876950 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.141079 0.099585 0.435685 0.323651
4 -1.226519 -0.508891 -0.876950 -1.620921 -0.184925 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.551227 0.038961 0.284271 0.125541
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1795 0.815316 -0.458035 1.140316 0.207758 2.823227 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.246454 0.129433 0.352837 0.271277
1796 0.815316 -0.203756 1.140316 -1.620921 2.196528 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.203554 0.061389 0.518578 0.216478
1797 0.815316 -0.508891 -0.876950 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.479310 0.068966 0.272414 0.179310
1798 -1.226519 -0.458035 1.140316 0.207758 -0.310264 0.0 0.0 0.0 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.286344 0.193833 0.330396 0.189427
1799 -1.226519 -0.508891 1.140316 -1.620921 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.286822 0.077519 0.275194 0.360465

1800 rows × 14653 columns

Machine Learning Models¶

Model training using LogisticRegression with best_params¶
  • {'logisticregressionverbose': 3, 'logisticregressionsolver': 'liblinear', 'logisticregressionpenalty': 'l2', 'logisticregressionmax_iter': 5000, 'logisticregression__C': 2.0}
In [109]:
# classifier_lr = LogisticRegression(verbose= 1,penalty = 'l2',max_iter= 1000, C =  3.0)
classifier_lr = LogisticRegression(verbose= 3,solver = 'liblinear', penalty = 'l2',max_iter= 5000, C =  2.0)
In [110]:
pipeline_final_lr = Pipeline(steps=[
#     ('pipe_my',my_sklearn_pipeline),
    ('LogisticRegression_classifier',classifier_lr)
    
                          ],
                             memory = 'tmp/cache'
                            )
In [111]:
pipeline_final_lr
Out[111]:
Pipeline(memory='tmp/cache',
         steps=[('LogisticRegression_classifier',
                 LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
                                    verbose=3))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('LogisticRegression_classifier',
                 LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
                                    verbose=3))])
LogisticRegression(C=2.0, max_iter=5000, solver='liblinear', verbose=3)
In [112]:
pipeline_final_lr.fit(df_X_train_transformed_resampled,y_train_resampled)
[LibLinear]
Out[112]:
Pipeline(memory='tmp/cache',
         steps=[('LogisticRegression_classifier',
                 LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
                                    verbose=3))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('LogisticRegression_classifier',
                 LogisticRegression(C=2.0, max_iter=5000, solver='liblinear',
                                    verbose=3))])
LogisticRegression(C=2.0, max_iter=5000, solver='liblinear', verbose=3)
In [113]:
y_pred_lr=pipeline_final_lr.predict(df_val_X_val_transformed)
In [114]:
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_lr, average= 'weighted') 
Out[114]:
0.37048772873553426
In [115]:
from sklearn.metrics import cohen_kappa_score

kp_lr=cohen_kappa_score(y_val,y_pred_lr)
kp_lr
Out[115]:
0.17754110260107603
In [ ]:
 
Model training using RandomForestClassifier with best_params¶
In [120]:
classifier_rf = RandomForestClassifier(n_estimators= 50, max_depth= 50, bootstrap= True)

# model_rf = make_pipeline(my_sklearn_pipeline, classifier_rf)
In [121]:
pipeline_final_rf = Pipeline(steps=[
#     ('pipe_my',my_sklearn_pipeline),
    ('RandomForest_Classifier',classifier_rf)
    
                          ],
                             memory = 'tmp/cache'
                            )
In [122]:
pipeline_final_rf.fit(df_X_train_transformed_resampled,y_train_resampled)
Out[122]:
Pipeline(memory='tmp/cache',
         steps=[('RandomForest_Classifier',
                 RandomForestClassifier(max_depth=50, n_estimators=50))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('RandomForest_Classifier',
                 RandomForestClassifier(max_depth=50, n_estimators=50))])
RandomForestClassifier(max_depth=50, n_estimators=50)
In [123]:
y_pred_rf=pipeline_final_rf.predict(df_val_X_val_transformed)
In [124]:
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_rf, average= 'weighted') 
Out[124]:
0.4094699812714273
In [125]:
from sklearn.metrics import cohen_kappa_score

kp_rf=cohen_kappa_score(y_val,y_pred_rf)
kp_rf
Out[125]:
0.19771296367318925
Model training using GradientBoostingClassifier with best_params¶
In [133]:
classifier_gb = GradientBoostingClassifier(n_estimators= 120, learning_rate = 0.2)

# model_gb = make_pipeline(my_sklearn_pipeline, classifier_gb)
In [134]:
pipeline_final_gb = Pipeline(steps=[
#     ('pipe_my',my_sklearn_pipeline),
    ('GradientBoosting_Classifier',classifier_gb)
    
                          ],
                             memory = 'tmp/cache'
                            )
In [135]:
pipeline_final_gb
Out[135]:
Pipeline(memory='tmp/cache',
         steps=[('GradientBoosting_Classifier',
                 GradientBoostingClassifier(learning_rate=0.2,
                                            n_estimators=120))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('GradientBoosting_Classifier',
                 GradientBoostingClassifier(learning_rate=0.2,
                                            n_estimators=120))])
GradientBoostingClassifier(learning_rate=0.2, n_estimators=120)
In [136]:
pipeline_final_gb.fit(df_X_train_transformed_resampled,y_train_resampled)
Out[136]:
Pipeline(memory='tmp/cache',
         steps=[('GradientBoosting_Classifier',
                 GradientBoostingClassifier(learning_rate=0.2,
                                            n_estimators=120))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('GradientBoosting_Classifier',
                 GradientBoostingClassifier(learning_rate=0.2,
                                            n_estimators=120))])
GradientBoostingClassifier(learning_rate=0.2, n_estimators=120)
In [137]:
y_pred_gb=pipeline_final_gb.predict(df_val_X_val_transformed)
In [138]:
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_gb, average= 'weighted') 
Out[138]:
0.3814493836551052
In [139]:
from sklearn.metrics import cohen_kappa_score

kp_gb=cohen_kappa_score(y_val,y_pred_gb)
kp_gb
Out[139]:
0.18104967629188817
In [ ]:
 
Model training using KNeighborsClassifier with best_params¶
In [126]:
classifier_knn = KNeighborsClassifier(n_neighbors= 4)
In [127]:
pipeline_final_knn = Pipeline(steps=[
#     ('pipe_my',my_sklearn_pipeline),
    ('KNeighbors_Classifier',classifier_knn)
    
                          ],
                             memory = 'tmp/cache'
                            )
In [128]:
pipeline_final_knn
Out[128]:
Pipeline(memory='tmp/cache',
         steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])
KNeighborsClassifier(n_neighbors=4)
In [129]:
pipeline_final_knn.fit(df_X_train_transformed_resampled,y_train_resampled)
Out[129]:
Pipeline(memory='tmp/cache',
         steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('KNeighbors_Classifier', KNeighborsClassifier(n_neighbors=4))])
KNeighborsClassifier(n_neighbors=4)
In [ ]:
 
In [130]:
y_pred_knn=pipeline_final_knn.predict(df_val_X_val_transformed)
In [131]:
from sklearn.metrics import precision_score
precision_score(y_val, y_pred_knn, average= 'weighted') 
Out[131]:
0.3370042872206625
In [132]:
from sklearn.metrics import cohen_kappa_score

kp_knn=cohen_kappa_score(y_val,y_pred_knn)
kp_knn
Out[132]:
0.12328131474431225
In [ ]:
 
In [ ]:
 

On TEST DATA¶

In [ ]:
 
In [142]:
df_test_copy.head(1)
Out[142]:
Type Age Gender Color1 Color2 Color3 MaturitySize FurLength Vaccinated Dewormed Sterilized Health Fee Description Images Breed
0 Cat 1.0 Male Black White Unknown Small Yes No No No Healthy 0.0 kitten for adoption, pls call for enquiry, off... C:\Users\praba\Documents\GitHub\UCA SEMESTER 2... Domestic_Short_Hair
In [143]:
df_test_copy["Images"][0]
Out[143]:
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\5df99d229-2.jpg'
In [144]:
df_test_copy_transformed = my_sklearn_pipeline.transform(df_test_copy)
In [145]:
import pandas as pd
dense_array_test = df_test_copy_transformed.toarray()


df_test_copy_transformed_dataframe = pd.DataFrame(dense_array_test)
In [146]:
df_test_copy_transformed_dataframe.head()
Out[146]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 -0.559747 1.140316 -1.620921 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.261236 0.070225 0.370787 0.297753
1 0.815316 -0.203756 1.140316 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.337393 0.076736 0.331303 0.254568
2 0.815316 -0.508891 -0.876950 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.503165 0.060127 0.164557 0.272152
3 0.815316 -0.458035 -0.876950 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.314961 0.031496 0.244094 0.409449
4 -1.226519 -0.458035 -0.876950 0.207758 -0.184925 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.440789 0.019737 0.210526 0.328947

5 rows × 14653 columns

save the transformed training data as csv so that I can use those during ml and dl training¶

df_test_copy_transformed as df_test_copy_transformed_dataframe dataframe is stored in df_test_copy_transformed_dataframe_df.csv¶

In [111]:
# df_test_copy_transformed_dataframe.to_csv('df_test_copy_transformed_dataframe_df.csv')
In [127]:
# df_test_copy_transformed_dataframe = pd.read_csv('df_test_copy_transformed_dataframe_df.csv')

Asthe highest kappa score I found by using RandomForestClassifier so finally I will use it for the test data prediction¶

In [147]:
y_pred_random_true = pipeline_final_rf.predict(df_test_copy_transformed_dataframe)
In [148]:
y_pred_logistic_true = pipeline_final_lr.predict(df_test_copy_transformed_dataframe)
y_pred_knn_true = pipeline_final_knn.predict(df_test_copy_transformed_dataframe)
y_pred_gbc_true = pipeline_final_gb.predict(df_test_copy_transformed_dataframe)
In [ ]:
 
In [322]:
models_pred = {}
models_pred['logistic'] = y_pred_logistic_true
models_pred['random'] = y_pred_random_true
models_pred['knn'] = y_pred_knn_true
# models_pred['gbc'] = y_pred_gbc_true
In [324]:
df_final_result_test_all = pd.DataFrame(models_pred)
print(df_final_result_test_all)
     logistic  random  knn
0         3.0     2.0  1.0
1         2.0     2.0  3.0
2         3.0     2.0  3.0
3         4.0     4.0  4.0
4         3.0     2.0  2.0
..        ...     ...  ...
495       4.0     2.0  4.0
496       1.0     1.0  1.0
497       4.0     4.0  4.0
498       4.0     4.0  4.0
499       4.0     2.0  0.0

[500 rows x 3 columns]
In [325]:
df_final_result_test_all.to_csv('df_final_result_test_all.csv')
In [ ]:
 

I got heighest kappa score using RandomForestClassifier, So i am finally predicting the result using RandomForestClassifier and storigthe data in csv file format¶

At the end of the project, you must submit:

an executer notebook writed like a report in order to explain your choice and your strategy your prediction for the test part (2 columns csv with id and prediction rate) the evaluation metric is the quadratic kappa score (available in sklearn)

In [286]:
df_y_pred_random_true = pd.DataFrame(y_pred_random_true,columns=['prediction rate'])
print(df_y_pred_random_true)
     prediction rate
0                2.0
1                2.0
2                2.0
3                4.0
4                2.0
..               ...
495              2.0
496              1.0
497              4.0
498              4.0
499              2.0

[500 rows x 1 columns]
In [303]:
# df_y_pred_random_true.to_csv(' result_ml.csv')
df_y_pred_random_true.to_csv(' result_ml.csv',index_label = 'id')
In [ ]:
# df_final_result_test_all['logistic'].to_csv(' result.csv', index=False)
In [ ]:
 
In [ ]:
 

Deep Learning Model¶

In [176]:
import pandas as pd


dense_array_X_train_transformed_resampled = X_train_transformed_resampled.toarray()

df_X_train_transformed_resampled = pd.DataFrame(dense_array)
df_X_train_transformed_resampled.head(2)
Out[176]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 0.101380 1.140316 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.298734 0.124051 0.281013 0.296203
1 -1.226519 1.220209 -0.876950 2.036438 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.471074 0.041322 0.247934 0.239669

2 rows × 14653 columns

In [177]:
import pandas as pd
dense_array_val_X_val_transformed = X_val_transformed.toarray()
df_val_X_val_transformed = pd.DataFrame(dense_array_val_X_val_transformed)
df_val_X_val_transformed.head(2)
Out[177]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 -0.559747 1.140316 -1.620921 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.285714 0.069426 0.353805 0.291055
1 0.815316 -0.051188 -0.876950 0.207758 2.196528 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.177449 0.052372 0.411583 0.358595

2 rows × 14653 columns

In [178]:
dense_array_X_train_transformed_resampled.shape
Out[178]:
(7802, 14653)
In [179]:
dense_array_val_X_val_transformed.shape
Out[179]:
(1800, 14653)

Model -1 (MLP)¶

In [180]:
#Dependencies
import keras
from keras.models import Sequential
from keras.layers import Dense
In [181]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Dropout

model_dl2 = Sequential([
    Input(shape=(dense_array_X_train_transformed_resampled.shape[1],)),
    Dense(1024, activation='relu'),
    Dropout(0.3),
        Dense(512, activation='relu'),
    Dropout(0.3),
    Dense(256, activation='relu'),
    Dropout(0.3),
    Dense(128, activation='relu'),
    Dropout(0.3),
    Dense(64, activation='relu'),
    Dropout(0.3),
    Dense(32, activation='relu'),
    Dense(5, activation='softmax')
])
In [200]:
# from tensorflow.keras.utils import plot_model
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
plot_model(model_dl2, to_file='model_plot2.png',
 show_shapes=True,
 show_layer_names=True,
 layer_range=None,
 show_layer_activations=True,
 show_trainable=True)
Out[200]:
In [182]:
model_dl2.summary()
Model: "sequential_2"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_14 (Dense)                     │ (None, 1024)                │      15,005,696 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_10 (Dropout)                 │ (None, 1024)                │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_15 (Dense)                     │ (None, 512)                 │         524,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_11 (Dropout)                 │ (None, 512)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_16 (Dense)                     │ (None, 256)                 │         131,328 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_12 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_17 (Dense)                     │ (None, 128)                 │          32,896 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_13 (Dropout)                 │ (None, 128)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_18 (Dense)                     │ (None, 64)                  │           8,256 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_14 (Dropout)                 │ (None, 64)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_19 (Dense)                     │ (None, 32)                  │           2,080 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_20 (Dense)                     │ (None, 5)                   │             165 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 15,705,221 (59.91 MB)
 Trainable params: 15,705,221 (59.91 MB)
 Non-trainable params: 0 (0.00 B)
In [183]:
model_dl2.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
In [184]:
from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

# Train the model with early stopping
history_model_dl2 = model_dl2.fit(dense_array_X_train_transformed_resampled, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 34s 148ms/step - accuracy: 0.2741 - loss: 1.5078 - val_accuracy: 0.2044 - val_loss: 2.0752
Epoch 2/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 30s 155ms/step - accuracy: 0.4151 - loss: 1.3329 - val_accuracy: 0.2261 - val_loss: 1.9477
Epoch 3/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 30s 153ms/step - accuracy: 0.5316 - loss: 1.1113 - val_accuracy: 0.2389 - val_loss: 1.8150
Epoch 4/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 144ms/step - accuracy: 0.6922 - loss: 0.7795 - val_accuracy: 0.4305 - val_loss: 1.6681
Epoch 5/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 145ms/step - accuracy: 0.7898 - loss: 0.5614 - val_accuracy: 0.5208 - val_loss: 1.6075
Epoch 6/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 29s 146ms/step - accuracy: 0.8702 - loss: 0.3818 - val_accuracy: 0.5349 - val_loss: 1.8078
Epoch 7/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 144ms/step - accuracy: 0.9043 - loss: 0.3143 - val_accuracy: 0.5279 - val_loss: 1.9766
Epoch 8/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 28s 143ms/step - accuracy: 0.9204 - loss: 0.2285 - val_accuracy: 0.5490 - val_loss: 2.3494
Epoch 9/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 32s 161ms/step - accuracy: 0.9337 - loss: 0.1956 - val_accuracy: 0.5400 - val_loss: 2.2032
Epoch 10/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 30s 152ms/step - accuracy: 0.9395 - loss: 0.1688 - val_accuracy: 0.5375 - val_loss: 2.5356
In [188]:
import matplotlib.pyplot as plt

plt.figure(figsize=(20, 6))

# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_dl2.history["loss"])
plt.plot(history_model_dl2.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")

# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_dl2.history["accuracy"])
plt.plot(history_model_dl2.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")

plt.show()

So ths deep learning model is overfitted

In [189]:
y_pred_val_model_dl2 = model_dl2.predict(dense_array_val_X_val_transformed)
57/57 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step
In [190]:
predicted_class_model_dl2 = np.argmax(y_pred_val_model_dl2, axis=1)
predicted_class_model_dl2
Out[190]:
array([1, 3, 2, ..., 2, 3, 1], dtype=int64)
In [191]:
from sklearn.metrics import cohen_kappa_score

kp_model_dl2=cohen_kappa_score(y_val,predicted_class_model_dl2)
kp_model_dl2
Out[191]:
0.16717386770164133
In [ ]:
 
In [ ]:
 

Model-2 (MLP)¶

In [271]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization
from tensorflow.keras import regularizers

model_dl3 = Sequential([
    Input(shape=(dense_array_X_train_transformed_resampled.shape[1],)),
    Dense(1024, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dense(5, activation='softmax')
])
In [272]:
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_dl3, to_file='model_plot3.png',
 show_shapes=True,
 show_layer_names=True,
 layer_range=None,
 show_layer_activations=True,
 show_trainable=True)
Out[272]:
In [273]:
model_dl3.summary()
Model: "sequential_6"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_22 (Dense)                     │ (None, 1024)                │      15,005,696 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_12               │ (None, 1024)                │           4,096 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_23 (Dropout)                 │ (None, 1024)                │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_23 (Dense)                     │ (None, 512)                 │         524,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_13               │ (None, 512)                 │           2,048 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_24 (Dropout)                 │ (None, 512)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_24 (Dense)                     │ (None, 256)                 │         131,328 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_14               │ (None, 256)                 │           1,024 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_25 (Dropout)                 │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_25 (Dense)                     │ (None, 128)                 │          32,896 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_15               │ (None, 128)                 │             512 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_26 (Dropout)                 │ (None, 128)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_26 (Dense)                     │ (None, 64)                  │           8,256 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_16               │ (None, 64)                  │             256 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_27 (Dropout)                 │ (None, 64)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_27 (Dense)                     │ (None, 32)                  │           2,080 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_17               │ (None, 32)                  │             128 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_28 (Dense)                     │ (None, 5)                   │             165 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 15,713,285 (59.94 MB)
 Trainable params: 15,709,253 (59.93 MB)
 Non-trainable params: 4,032 (15.75 KB)
In [274]:
model_dl3.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
In [ ]:
 
In [275]:
from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

history_model_dl3 = model_dl3.fit(dense_array_X_train_transformed_resampled, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 46s 204ms/step - accuracy: 0.2359 - loss: 7.2854 - val_accuracy: 0.1749 - val_loss: 7.3374
Epoch 2/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 37s 191ms/step - accuracy: 0.3260 - loss: 6.8489 - val_accuracy: 0.2159 - val_loss: 6.6761
Epoch 3/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 38s 192ms/step - accuracy: 0.3846 - loss: 5.8009 - val_accuracy: 0.2242 - val_loss: 5.7876
Epoch 4/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 209ms/step - accuracy: 0.4397 - loss: 4.7753 - val_accuracy: 0.2249 - val_loss: 5.0507
Epoch 5/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 214ms/step - accuracy: 0.4891 - loss: 4.0082 - val_accuracy: 0.2268 - val_loss: 4.6268
Epoch 6/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 210ms/step - accuracy: 0.5415 - loss: 3.4224 - val_accuracy: 0.2338 - val_loss: 4.1884
Epoch 7/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 213ms/step - accuracy: 0.5832 - loss: 3.0524 - val_accuracy: 0.2569 - val_loss: 3.8318
Epoch 8/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 210ms/step - accuracy: 0.6217 - loss: 2.7813 - val_accuracy: 0.2774 - val_loss: 3.7865
Epoch 9/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 216ms/step - accuracy: 0.6509 - loss: 2.7133 - val_accuracy: 0.3318 - val_loss: 3.6195
Epoch 10/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 40s 203ms/step - accuracy: 0.6718 - loss: 2.6204 - val_accuracy: 0.3261 - val_loss: 3.7071
Epoch 11/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 39s 198ms/step - accuracy: 0.6846 - loss: 2.5948 - val_accuracy: 0.3927 - val_loss: 3.5583
Epoch 12/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 208ms/step - accuracy: 0.6977 - loss: 2.5771 - val_accuracy: 0.4183 - val_loss: 3.4240
Epoch 13/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 214ms/step - accuracy: 0.7212 - loss: 2.5433 - val_accuracy: 0.4273 - val_loss: 3.4747
Epoch 14/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 44s 226ms/step - accuracy: 0.7304 - loss: 2.5124 - val_accuracy: 0.3299 - val_loss: 3.8769
Epoch 15/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 42s 212ms/step - accuracy: 0.7457 - loss: 2.5853 - val_accuracy: 0.4254 - val_loss: 3.6232
Epoch 16/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 43s 217ms/step - accuracy: 0.7475 - loss: 2.5582 - val_accuracy: 0.5010 - val_loss: 3.4119
Epoch 17/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 41s 211ms/step - accuracy: 0.7672 - loss: 2.4889 - val_accuracy: 0.4600 - val_loss: 3.4950
Epoch 18/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 40s 203ms/step - accuracy: 0.7680 - loss: 2.4787 - val_accuracy: 0.4202 - val_loss: 3.6264
Epoch 19/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 37s 187ms/step - accuracy: 0.7662 - loss: 2.5353 - val_accuracy: 0.4222 - val_loss: 3.7756
Epoch 20/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 37s 188ms/step - accuracy: 0.7672 - loss: 2.5306 - val_accuracy: 0.4837 - val_loss: 3.5081
Epoch 21/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 38s 193ms/step - accuracy: 0.7785 - loss: 2.4854 - val_accuracy: 0.4856 - val_loss: 3.4491
In [276]:
import matplotlib.pyplot as plt

plt.figure(figsize=(20, 6))

# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_dl3.history["loss"])
plt.plot(history_model_dl3.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")

# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_dl3.history["accuracy"])
plt.plot(history_model_dl3.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")

plt.show()
In [277]:
y_pred_val_model_dl3 = model_dl3.predict(dense_array_val_X_val_transformed)
57/57 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step
In [278]:
predicted_class_model_dl3 = np.argmax(y_pred_val_model_dl3, axis=1)
predicted_class_model_dl3
Out[278]:
array([1, 4, 2, ..., 3, 2, 0], dtype=int64)
In [279]:
from sklearn.metrics import cohen_kappa_score

kp_model_dl3=cohen_kappa_score(y_val,predicted_class_model_dl3)
kp_model_dl3
Out[279]:
0.20020809195432487
In [ ]:
 
In [ ]:
 

MODEL-3 (Budirectional LSTM)¶

In [241]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Bidirectional, LSTM, Dense, Dropout
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

dense_array_X_train_transformed_resampled_reshaped = dense_array_X_train_transformed_resampled.reshape(dense_array_X_train_transformed_resampled.shape[0], 1, dense_array_X_train_transformed_resampled.shape[1])


# Build the Bidirectional LSTM model
model_lstm = Sequential([
    Bidirectional(LSTM(64, return_sequences=True), input_shape=(dense_array_X_train_transformed_resampled_reshaped.shape[1], dense_array_val_X_val_transformed_reshaped.shape[2])),
    Dropout(0.5),
    Bidirectional(LSTM(32)),
    Dropout(0.5),
    Dense(64, activation='relu'),
    Dropout(0.5),
    Dense(5, activation='softmax')
])
In [242]:
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_lstm, to_file='model_plot_lstm.png',
 show_shapes=True,
 show_layer_names=True,
 layer_range=None,
 show_layer_activations=True,
 show_trainable=True)
Out[242]:
In [243]:
model_lstm.summary()
Model: "sequential_3"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ bidirectional_4 (Bidirectional)      │ (None, 1, 128)              │       7,535,616 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_12 (Dropout)                 │ (None, 1, 128)              │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bidirectional_5 (Bidirectional)      │ (None, 64)                  │          41,216 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_13 (Dropout)                 │ (None, 64)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_11 (Dense)                     │ (None, 64)                  │           4,160 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_14 (Dropout)                 │ (None, 64)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_12 (Dense)                     │ (None, 5)                   │             325 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 7,581,317 (28.92 MB)
 Trainable params: 7,581,317 (28.92 MB)
 Non-trainable params: 0 (0.00 B)
In [244]:
# Compile the model
model_lstm.compile(optimizer='adam',
                   loss='sparse_categorical_crossentropy',
                   metrics=['accuracy'])

#
In [245]:
# dense_array_val_X_val_transformed_reshaped.shape
In [246]:
from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

# Train the model with early stopping
# history_model_dl3 = model_dl3.fit(df, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
history_model_lstm = model_lstm.fit(dense_array_X_train_transformed_resampled_reshaped, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 24s 88ms/step - accuracy: 0.2715 - loss: 1.5333 - val_accuracy: 0.2012 - val_loss: 2.0395
Epoch 2/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 80ms/step - accuracy: 0.3717 - loss: 1.3764 - val_accuracy: 0.2422 - val_loss: 1.8430
Epoch 3/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 82ms/step - accuracy: 0.5013 - loss: 1.1929 - val_accuracy: 0.2639 - val_loss: 1.7744
Epoch 4/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 17s 84ms/step - accuracy: 0.6173 - loss: 0.9766 - val_accuracy: 0.3530 - val_loss: 1.7644
Epoch 5/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 82ms/step - accuracy: 0.7000 - loss: 0.7893 - val_accuracy: 0.4158 - val_loss: 1.8243
Epoch 6/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 15s 78ms/step - accuracy: 0.7588 - loss: 0.6479 - val_accuracy: 0.4869 - val_loss: 1.8464
Epoch 7/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 15s 78ms/step - accuracy: 0.8180 - loss: 0.5200 - val_accuracy: 0.4990 - val_loss: 2.0458
Epoch 8/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 15s 78ms/step - accuracy: 0.8430 - loss: 0.4533 - val_accuracy: 0.5106 - val_loss: 2.2172
Epoch 9/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 16s 79ms/step - accuracy: 0.8464 - loss: 0.4223 - val_accuracy: 0.5208 - val_loss: 2.1710
In [251]:
import matplotlib.pyplot as plt

plt.figure(figsize=(20, 6))

# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_lstm.history["loss"])
plt.plot(history_model_lstm.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")

# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_lstm.history["accuracy"])
plt.plot(history_model_lstm.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")

plt.show()

So ths deep learning model is overfitted

In [247]:
dense_array_val_X_val_transformed_reshaped = dense_array_val_X_val_transformed.reshape(dense_array_val_X_val_transformed.shape[0], 1, dense_array_val_X_val_transformed.shape[1])
In [248]:
y_pred_val_model_lstm = model_lstm.predict(dense_array_val_X_val_transformed_reshaped)
57/57 ━━━━━━━━━━━━━━━━━━━━ 2s 17ms/step
In [249]:
predicted_class_model_lstm = np.argmax(y_pred_val_model_lstm, axis=1)
predicted_class_model_lstm
Out[249]:
array([1, 2, 2, ..., 3, 2, 1], dtype=int64)

KAPPA SCORE

In [250]:
from sklearn.metrics import cohen_kappa_score

kp_model_lstm=cohen_kappa_score(y_val,predicted_class_model_lstm)
kp_model_lstm
Out[250]:
0.1836346383642512
In [ ]:
 
In [ ]:
 

MODEL-4 (LSTM)¶

In [252]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# dense_array_X_train_transformed_resampled_reshaped = dense_array_X_train_transformed_resampled.reshape(dense_array_X_train_transformed_resampled.shape[0], 1, dense_array_X_train_transformed_resampled.shape[1])


# Build the LSTM model
model_lstm_normal = Sequential([
    LSTM(64, return_sequences=True, input_shape=(dense_array_X_train_transformed_resampled_reshaped.shape[1], dense_array_X_train_transformed_resampled_reshaped.shape[2])),
    Dropout(0.5),
    LSTM(32),
    Dropout(0.5),
    Dense(64, activation='relu'),
    Dropout(0.5),
    Dense(5, activation='softmax')
])
In [253]:
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_lstm_normal, to_file='model_plot_lstm_normal.png',
 show_shapes=True,
 show_layer_names=True,
 layer_range=None,
 show_layer_activations=True,
 show_trainable=True)
Out[253]:
In [254]:
model_lstm_normal.summary()
Model: "sequential_4"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ lstm_6 (LSTM)                        │ (None, 1, 64)               │       3,767,808 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_15 (Dropout)                 │ (None, 1, 64)               │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_7 (LSTM)                        │ (None, 32)                  │          12,416 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_16 (Dropout)                 │ (None, 32)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_13 (Dense)                     │ (None, 64)                  │           2,112 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_17 (Dropout)                 │ (None, 64)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_14 (Dense)                     │ (None, 5)                   │             325 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 3,782,661 (14.43 MB)
 Trainable params: 3,782,661 (14.43 MB)
 Non-trainable params: 0 (0.00 B)
In [255]:
# Compile the model
model_lstm_normal.compile(optimizer='adam',
                   loss='sparse_categorical_crossentropy',
                   metrics=['accuracy'])

#
In [256]:
from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

# Train the model with early stopping
# history_model_dl3 = model_dl3.fit(df, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
history_model_lstm = model_lstm_normal.fit(dense_array_X_train_transformed_resampled_reshaped, y_train_resampled, epochs=50, validation_split=0.2, batch_size=32, callbacks=[early_stopping])
Epoch 1/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.2498 - loss: 1.5455 - val_accuracy: 0.2037 - val_loss: 2.0815
Epoch 2/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 9s 43ms/step - accuracy: 0.3456 - loss: 1.4266 - val_accuracy: 0.2088 - val_loss: 1.9354
Epoch 3/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.4676 - loss: 1.2488 - val_accuracy: 0.2274 - val_loss: 1.8445
Epoch 4/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.5546 - loss: 1.0982 - val_accuracy: 0.2319 - val_loss: 1.7639
Epoch 5/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 9s 44ms/step - accuracy: 0.6234 - loss: 0.9351 - val_accuracy: 0.2844 - val_loss: 1.7828
Epoch 6/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 42ms/step - accuracy: 0.7003 - loss: 0.7959 - val_accuracy: 0.4023 - val_loss: 1.7832
Epoch 7/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.7515 - loss: 0.6904 - val_accuracy: 0.4478 - val_loss: 1.8802
Epoch 8/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 8s 43ms/step - accuracy: 0.7787 - loss: 0.6132 - val_accuracy: 0.4888 - val_loss: 1.8402
Epoch 9/50
196/196 ━━━━━━━━━━━━━━━━━━━━ 9s 43ms/step - accuracy: 0.8096 - loss: 0.5284 - val_accuracy: 0.4901 - val_loss: 2.0235
In [257]:
import matplotlib.pyplot as plt

plt.figure(figsize=(20, 6))

# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_lstm.history["loss"])
plt.plot(history_model_lstm.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")

# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_lstm.history["accuracy"])
plt.plot(history_model_lstm.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")

plt.show()

So ths deep learning model is overfitted

In [ ]:
# dense_array_val_X_val_transformed_reshaped = dense_array_val_X_val_transformed.reshape(dense_array_val_X_val_transformed.shape[0], 1, dense_array_val_X_val_transformed.shape[1])
In [258]:
y_pred_val_model_lstm_normal = model_lstm_normal.predict(dense_array_val_X_val_transformed_reshaped)
57/57 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step
In [259]:
predicted_class_model_lstm_normal = np.argmax(y_pred_val_model_lstm_normal, axis=1)
predicted_class_model_lstm_normal
Out[259]:
array([1, 2, 2, ..., 3, 2, 1], dtype=int64)

KAPPA SCORE

In [260]:
from sklearn.metrics import cohen_kappa_score

kp_model_lstm=cohen_kappa_score(y_val,predicted_class_model_lstm_normal)
kp_model_lstm
Out[260]:
0.20079267785056332
In [ ]:
 
In [ ]:
 
In [ ]:
 

After verifying these 4 deep learning models I found that model 2 (MLP) is working better among others¶

In [ ]:
 

On TEST DATA¶

In [281]:
df_test_copy.head(1)
Out[281]:
Type Age Gender Color1 Color2 Color3 MaturitySize FurLength Vaccinated Dewormed Sterilized Health Fee Description Images Breed
0 Cat 1.0 Male Black White Unknown Small Yes No No No Healthy 0.0 kitten for adoption, pls call for enquiry, off... C:\Users\praba\Documents\GitHub\UCA SEMESTER 2... Domestic_Short_Hair
In [282]:
df_test_copy["Images"][0]
Out[282]:
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\test_images_all\\5df99d229-2.jpg'
In [283]:
df_test_copy_transformed = my_sklearn_pipeline.transform(df_test_copy)
In [284]:
import pandas as pd
dense_array_test = df_test_copy_transformed.toarray()


df_test_copy_transformed_dataframe = pd.DataFrame(dense_array_test)
In [285]:
df_test_copy_transformed_dataframe.head()
Out[285]:
0 1 2 3 4 5 6 7 8 9 ... 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
0 -1.226519 -0.559747 1.140316 -1.620921 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.261236 0.070225 0.370787 0.297753
1 0.815316 -0.203756 1.140316 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.337393 0.076736 0.331303 0.254568
2 0.815316 -0.508891 -0.876950 0.207758 -0.310264 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.503165 0.060127 0.164557 0.272152
3 0.815316 -0.458035 -0.876950 0.207758 -0.310264 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.314961 0.031496 0.244094 0.409449
4 -1.226519 -0.458035 -0.876950 0.207758 -0.184925 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.440789 0.019737 0.210526 0.328947

5 rows × 14653 columns

In [ ]:
 

save the transformed training data as csv so that I can use those during ml and dl training¶

df_test_copy_transformed as df_test_copy_transformed_dataframe dataframe is stored in df_test_copy_transformed_dataframe_df.csv¶

In [111]:
# df_test_copy_transformed_dataframe.to_csv('df_test_copy_transformed_dataframe_df.csv')
In [127]:
# df_test_copy_transformed_dataframe = pd.read_csv('df_test_copy_transformed_dataframe_df.csv')

As the highest kappa score I found by using model 2 (MLP) so finally I will use it for the test data prediction¶

In [290]:
# dense_array_test
In [292]:
y_pred_MLP_model2_true = model_dl3.predict(dense_array_test)
16/16 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step
In [297]:
y_pred_MLP_model2_true.shape
Out[297]:
(500, 5)
In [298]:
predicted_class_model_model2_test = np.argmax(y_pred_MLP_model2_true, axis=1)
predicted_class_model_model2_test
Out[298]:
array([2, 2, 2, 2, 3, 1, 4, 2, 1, 1, 3, 2, 1, 4, 2, 3, 2, 3, 1, 4, 1, 3,
       1, 1, 2, 1, 4, 4, 4, 3, 2, 1, 4, 2, 3, 4, 3, 1, 1, 2, 4, 3, 4, 2,
       4, 1, 1, 4, 4, 4, 4, 2, 4, 1, 4, 4, 4, 4, 4, 4, 1, 1, 4, 4, 2, 4,
       2, 3, 4, 4, 3, 2, 2, 1, 3, 1, 2, 1, 4, 4, 4, 2, 4, 2, 1, 1, 4, 4,
       2, 3, 1, 4, 1, 4, 2, 2, 2, 2, 4, 2, 3, 4, 4, 4, 3, 2, 2, 2, 3, 3,
       1, 4, 2, 2, 1, 2, 4, 1, 4, 4, 3, 4, 2, 2, 4, 1, 2, 1, 2, 3, 4, 2,
       1, 2, 3, 4, 2, 3, 4, 1, 3, 2, 1, 2, 2, 1, 4, 3, 2, 3, 2, 3, 3, 3,
       1, 3, 2, 4, 2, 4, 1, 2, 4, 3, 2, 4, 1, 3, 4, 4, 4, 4, 4, 3, 4, 2,
       3, 1, 4, 4, 2, 2, 2, 2, 2, 2, 3, 1, 4, 4, 2, 4, 1, 4, 2, 1, 3, 3,
       2, 4, 4, 1, 4, 2, 3, 2, 2, 4, 4, 2, 3, 2, 4, 2, 4, 2, 4, 4, 2, 2,
       2, 4, 2, 4, 4, 2, 4, 4, 2, 2, 4, 1, 4, 2, 2, 4, 1, 4, 2, 3, 4, 2,
       1, 3, 2, 4, 1, 3, 3, 1, 2, 4, 2, 4, 4, 4, 4, 1, 2, 2, 1, 2, 4, 2,
       4, 1, 2, 4, 2, 2, 1, 3, 2, 4, 3, 2, 4, 2, 1, 2, 2, 2, 1, 4, 4, 2,
       2, 2, 3, 2, 4, 2, 3, 1, 4, 2, 2, 3, 4, 1, 4, 3, 4, 2, 4, 2, 1, 2,
       1, 3, 1, 4, 2, 2, 2, 1, 4, 4, 3, 2, 2, 3, 4, 2, 4, 4, 2, 3, 2, 2,
       2, 4, 4, 1, 4, 4, 1, 1, 4, 3, 4, 2, 2, 4, 4, 2, 2, 2, 4, 4, 4, 4,
       4, 1, 2, 4, 2, 1, 4, 3, 2, 1, 2, 4, 4, 4, 1, 1, 4, 4, 3, 3, 1, 3,
       4, 1, 4, 4, 2, 4, 3, 2, 3, 2, 3, 4, 1, 1, 2, 4, 2, 2, 2, 4, 4, 2,
       2, 2, 4, 3, 3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 2, 3, 4, 2, 3, 4,
       2, 3, 1, 4, 3, 4, 1, 2, 2, 2, 4, 4, 2, 2, 4, 2, 4, 3, 4, 4, 4, 3,
       4, 4, 3, 2, 4, 3, 2, 3, 1, 4, 2, 3, 2, 1, 3, 3, 4, 1, 3, 4, 3, 3,
       3, 4, 4, 2, 3, 2, 4, 2, 2, 2, 1, 3, 2, 4, 1, 2, 3, 4, 4, 3, 4, 4,
       4, 4, 2, 3, 2, 3, 2, 4, 1, 2, 1, 4, 1, 4, 4, 3], dtype=int64)
In [ ]:
 
In [ ]:
 
In [299]:
models_pred_dl = {}
models_pred_dl['prediction rate'] = predicted_class_model_model2_test
In [300]:
df_final_result_test_dl = pd.DataFrame(models_pred_dl)
print(df_final_result_test_dl)
     prediction rate
0                  2
1                  2
2                  2
3                  2
4                  3
..               ...
495                4
496                1
497                4
498                4
499                3

[500 rows x 1 columns]
In [302]:
df_final_result_test_dl.to_csv(' result_dl.csv',index_label = 'id')
In [ ]:
 
In [ ]:
 
In [ ]:
 

__ END _

In [ ]:
 

I was planning to use Vgg16 for image extraction but didnot use that as its a pretrained model¶

I only processed the training dataset¶

i haven't porcessed the test dataset¶

In [351]:
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.applications import VGG16
from keras.models import Model
In [352]:
base_model = VGG16(weights='imagenet', include_top=False)
In [353]:
model = Model(inputs=base_model.input, outputs=base_model.output)
In [354]:
import numpy as np

# Function to extract features from a batch of images
def extract_features_batch(img_paths):
    batch_features = []
    for img_path in img_paths:
        img = image.load_img(img_path, target_size=(224, 224))  # Resize image to match VGG16 input size
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = preprocess_input(x)  # Preprocess input based on VGG16 requirements
        features = model.predict(x)
        batch_features.append(features.flatten())  # Flatten the features into a 1D array
    return np.array(batch_features)
In [355]:
# df_train_copy['Images'].shape
df_train_copy['Images'][0]
Out[355]:
'C:\\Users\\praba\\Documents\\GitHub\\UCA SEMESTER 2 M1\\deeplearning 2\\final project and lab\\drive-download-20240301T144626Z-001\\train_images_all\\3b178aa59-5.jpg'
In [356]:
df_train_copy['Images'].shape[0]
Out[356]:
9000

batch size is 32 used as its not possible to process all 9000 images in a singal batch

In [357]:
# Process images in batches of size 64
batch_size = 32
num_images = df_train_copy['Images'].shape[0]

num_batches = (num_images + batch_size - 1) // batch_size

image_features = []

# Measure the time taken to execute the loop
%time
for batch_index in range(num_batches):
    start_index = batch_index * batch_size
    end_index = min((batch_index + 1) * batch_size, num_images)
    batch_img_paths = df_train_copy['Images'][start_index:end_index]
    batch_features = extract_features_batch(batch_img_paths)
    image_features.extend(batch_features)

# print("Shape of extracted features:", len(image_features))
CPU times: total: 0 ns
Wall time: 0 ns
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 279ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 402ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 435ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 425ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 481ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 409ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 383ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 433ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 392ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 399ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 462ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 433ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 392ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 434ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 433ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 389ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 287ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 406ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 402ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 276ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 459ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 423ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 402ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 409ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 441ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 391ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 402ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 411ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 448ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 382ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 466ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 417ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 409ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 414ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 418ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 411ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 485ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 411ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 412ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 494ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 399ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 383ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 389ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 429ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 409ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 383ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 408ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 438ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 404ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 391ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 397ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 389ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 450ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 456ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 414ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 397ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 392ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 425ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 420ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 382ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 418ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 444ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 449ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 439ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 453ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 459ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 498ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 432ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 402ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 383ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 389ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 438ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 468ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 408ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 399ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 419ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 418ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 389ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 392ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 285ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 382ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 273ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 286ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 275ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 382ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 450ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 427ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 286ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 409ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 425ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 398ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 382ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 391ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 419ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 285ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 273ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 286ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 411ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 273ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 271ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 284ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 474ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 425ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 435ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 397ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 402ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 428ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 396ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 389ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 441ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 397ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 383ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 389ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 276ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 411ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 275ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 413ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 382ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 404ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 449ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 391ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 279ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 279ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 419ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 423ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 418ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 274ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 285ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 418ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 276ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 277ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 441ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 276ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 419ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 418ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 398ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 273ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 285ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 383ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 411ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 319ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 421ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 279ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 280ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 287ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 285ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 412ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 460ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 433ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 377ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 434ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 375ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 428ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 412ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 394ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 443ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 443ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 447ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 401ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 409ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 416ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 408ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 326ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 427ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 403ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 376ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 311ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 372ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 341ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 283ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 417ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 417ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 395ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 384ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 414ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 447ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 417ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 433ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 474ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 410ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 570ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 449ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 328ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 335ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 327ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 404ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 312ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 417ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 458ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 306ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 323ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 329ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 309ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 336ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 402ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 302ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 347ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 324ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 315ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 381ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 390ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 426ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 656ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 425ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 433ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 457ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 386ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 420ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 433ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 382ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 391ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 393ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 620ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 495ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 537ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 332ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 447ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 348ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 361ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 374ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 371ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 367ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 369ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 362ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 342ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 338ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 333ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 392ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 385ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 397ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 368ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 344ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 373ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 365ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 379ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 331ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 339ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 366ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 334ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 337ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 349ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 352ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 354ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 346ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 351ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 444ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 340ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 358ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 350ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 343ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 357ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 345ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 353ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 415ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 363ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 383ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 364ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 378ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 387ms/step
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step
In [360]:
image_features_array = np.array(image_features)
In [361]:
import pandas as pd

df_vgg = pd.DataFrame(image_features_array)

print("Shape of DataFrame:", df_vgg.shape)
Shape of DataFrame: (9000, 25088)
In [ ]:
 
In [385]:
# Creating pipelines for numerical and categorical features
numerical_pipeline_dl = Pipeline(steps=[
    ('stdscaler', StandardScaler())
])

categorical_pipeline_dl = Pipeline(steps=[
    ('onehot', OneHotEncoder(handle_unknown='ignore'))  # One-hot encoding categorical features
])

# Define pipeline for text features
text_transformer_pipeline_dl = Pipeline(steps=[ ('vectorizer', CountVectorizer(max_features=1000))])



col_transformer_dl = ColumnTransformer(transformers=[
   ('drop_columns','drop',drop_features),

    ('numerical', numerical_pipeline_dl, numerical_features),
    ('categorical', categorical_pipeline_dl, categorical_features),
    
    ('text-data', text_transformer_pipeline_dl, 'Description')
#     
    
])


    
my_sklearn_pipeline_dl = Pipeline(steps=[
    ('TypeConverter', TypeConverter()),
    ('age', AgeConverter()),
    ('text_cleaner', TextCleaner()),
    ('MaturitySizeConverter' ,MaturitySizeConverter()),
    ("GenderConverter",GenderConverter()),

                 ('col_transformer',col_transformer_dl)
    ]

                           )
In [386]:
my_sklearn_pipeline_dl
Out[386]:
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'Age', 'Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer(max_features=1000))]),
                                                  'Description')]))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'Age', 'Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer(max_features=1000))]),
                                                  'Description')]))])
TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
                                ('numerical',
                                 Pipeline(steps=[('stdscaler',
                                                  StandardScaler())]),
                                 ['Type', 'Age', 'Gender', 'MaturitySize',
                                  'Fee']),
                                ('categorical',
                                 Pipeline(steps=[('onehot',
                                                  OneHotEncoder(handle_unknown='ignore'))]),
                                 ['Color1', 'Color2', 'FurLength', 'Vaccinated',
                                  'Dewormed', 'Sterilized', 'Health',
                                  'Breed']),
                                ('text-data',
                                 Pipeline(steps=[('vectorizer',
                                                  CountVectorizer(max_features=1000))]),
                                 'Description')])
['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer(max_features=1000)
In [388]:
X.shape
Out[388]:
(9000, 16)
In [389]:
#  fits the sklearn pipeline `my_sklearn_pipeline` to the training data X_train.

my_sklearn_pipeline_dl.fit(X)
Out[389]:
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'Age', 'Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer(max_features=1000))]),
                                                  'Description')]))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('TypeConverter', TypeConverter()), ('age', AgeConverter()),
                ('text_cleaner', TextCleaner()),
                ('MaturitySizeConverter', MaturitySizeConverter()),
                ('GenderConverter', GenderConverter()),
                ('col_transformer',
                 ColumnTransformer(transformers=[('drop_columns', 'drop',
                                                  ['Color3']),
                                                 ('numerical',
                                                  Pipeline(steps=[('stdscaler',
                                                                   StandardScaler())]),
                                                  ['Type', 'Age', 'Gender',
                                                   'MaturitySize', 'Fee']),
                                                 ('categorical',
                                                  Pipeline(steps=[('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['Color1', 'Color2',
                                                   'FurLength', 'Vaccinated',
                                                   'Dewormed', 'Sterilized',
                                                   'Health', 'Breed']),
                                                 ('text-data',
                                                  Pipeline(steps=[('vectorizer',
                                                                   CountVectorizer(max_features=1000))]),
                                                  'Description')]))])
TypeConverter()
AgeConverter()
TextCleaner()
MaturitySizeConverter()
GenderConverter()
ColumnTransformer(transformers=[('drop_columns', 'drop', ['Color3']),
                                ('numerical',
                                 Pipeline(steps=[('stdscaler',
                                                  StandardScaler())]),
                                 ['Type', 'Age', 'Gender', 'MaturitySize',
                                  'Fee']),
                                ('categorical',
                                 Pipeline(steps=[('onehot',
                                                  OneHotEncoder(handle_unknown='ignore'))]),
                                 ['Color1', 'Color2', 'FurLength', 'Vaccinated',
                                  'Dewormed', 'Sterilized', 'Health',
                                  'Breed']),
                                ('text-data',
                                 Pipeline(steps=[('vectorizer',
                                                  CountVectorizer(max_features=1000))]),
                                 'Description')])
['Color3']
drop
['Type', 'Age', 'Gender', 'MaturitySize', 'Fee']
StandardScaler()
['Color1', 'Color2', 'FurLength', 'Vaccinated', 'Dewormed', 'Sterilized', 'Health', 'Breed']
OneHotEncoder(handle_unknown='ignore')
Description
CountVectorizer(max_features=1000)
In [390]:
X_train_transform_dl = my_sklearn_pipeline_dl.transform(X)
In [391]:
X_train_transform_dl
Out[391]:
<9000x1187 sparse matrix of type '<class 'numpy.float64'>'
	with 321553 stored elements in Compressed Sparse Row format>
In [392]:
import pandas as pd


dense_arrayX_train_transform_dl = X_train_transform_dl.toarray()

df_dense_arrayX_train_transform_dl = pd.DataFrame(dense_arrayX_train_transform_dl)
df_dense_arrayX_train_transform_dl.head(2)
Out[392]:
0 1 2 3 4 5 6 7 8 9 ... 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186
0 0.820282 3.720374 1.129934 -1.626893 -0.299511 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.820282 -0.557090 -0.885007 0.208409 0.313454 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0

2 rows × 1187 columns

In [ ]:
 
In [393]:
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler, MinMaxScaler



pipeline_final_pca = Pipeline(steps=[
#     ('pipe_my',my_sklearn_pipeline),
    
     ('scaler', MinMaxScaler()),  # Step 1: MinMaxScaler the features
    ('pca', PCA(n_components=5))   # Step 5: Apply PCA with 2 components
    
                          ],
                             memory = 'tmp/cache'
                            )
In [394]:
pipeline_final_pca
Out[394]:
Pipeline(memory='tmp/cache',
         steps=[('scaler', MinMaxScaler()), ('pca', PCA(n_components=5))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(memory='tmp/cache',
         steps=[('scaler', MinMaxScaler()), ('pca', PCA(n_components=5))])
MinMaxScaler()
PCA(n_components=5)
In [395]:
# Now you can fit the pipeline to your data and transform it
# Assuming X is your data matrix
pca_result = pipeline_final_pca.fit_transform(df_vgg)
In [ ]:
 
In [396]:
pca_result.shape
Out[396]:
(9000, 5)
In [397]:
import pandas as pd

df_vgg_pca = pd.DataFrame(pca_result)

print("Shape of DataFrame:", df_vgg_pca.shape)
Shape of DataFrame: (9000, 5)
In [405]:
result_final_data = pd.concat([df_dense_arrayX_train_transform_dl, df_vgg_pca,y], axis=1)
In [406]:
result_final_data.shape
Out[406]:
(9000, 1193)
In [483]:
result_final_data.head(2)
Out[483]:
0 1 2 3 4 5 6 7 8 9 ... 1183 1184 1185 1186 0 1 2 3 4 AdoptionSpeed
0 0.820282 3.720374 1.129934 -1.626893 -0.299511 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.013023 -0.589681 0.710931 0.651019 -1.138258 4.0
1 0.820282 -0.557090 -0.885007 0.208409 0.313454 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 0.0 1.744552 0.926937 -1.067492 0.113089 -0.893152 3.0

2 rows × 1193 columns

In [486]:
import pandas as pd

result_final_data.columns = range(len(result_final_data.columns))

print(result_final_data)
          0         1         2         3         4     5     6     7     \
0     0.820282  3.720374  1.129934 -1.626893 -0.299511   0.0   1.0   0.0   
1     0.820282 -0.557090 -0.885007  0.208409  0.313454   1.0   0.0   0.0   
2     0.820282 -0.557090  1.129934  0.208409 -0.299511   0.0   1.0   0.0   
3     0.820282 -0.454018  1.129934  0.208409 -0.299511   1.0   0.0   0.0   
4     0.820282 -0.196340  1.129934  2.043710 -0.299511   0.0   1.0   0.0   
...        ...       ...       ...       ...       ...   ...   ...   ...   
8995  0.820282  1.298196 -0.885007  0.208409 -0.299511   0.0   0.0   1.0   
8996 -1.219093 -0.299411  1.129934  0.208409 -0.299511   0.0   0.0   0.0   
8997 -1.219093 -0.350947  1.129934 -1.626893 -0.299511   1.0   0.0   0.0   
8998  0.820282  0.009803  1.129934  0.208409 -0.299511   0.0   1.0   0.0   
8999  0.820282 -0.505554 -0.885007  0.208409 -0.299511   1.0   0.0   0.0   

      8     9     ...  1183  1184  1185  1186      1187      1188      1189  \
0      0.0   0.0  ...   0.0   0.0   0.0   0.0  1.013023 -0.589681  0.710931   
1      0.0   0.0  ...   0.0   0.0   1.0   0.0  1.744552  0.926937 -1.067492   
2      0.0   0.0  ...   0.0   0.0   0.0   0.0  2.968973 -0.419956  0.513002   
3      0.0   0.0  ...   0.0   0.0   0.0   0.0  0.303877 -0.815112  0.252377   
4      0.0   0.0  ...   0.0   0.0   0.0   0.0  0.616000 -0.269426  0.987940   
...    ...   ...  ...   ...   ...   ...   ...       ...       ...       ...   
8995   0.0   0.0  ...   0.0   0.0   0.0   0.0  0.310028 -0.752743  0.259324   
8996   0.0   0.0  ...   0.0   0.0   0.0   0.0 -0.815235  0.466691 -1.140541   
8997   0.0   0.0  ...   0.0   0.0   0.0   0.0 -0.966591  0.645182  2.310626   
8998   0.0   0.0  ...   0.0   0.0   1.0   0.0  2.535786 -1.612642 -0.228553   
8999   0.0   0.0  ...   0.0   0.0   0.0   0.0  1.669739 -0.084968 -1.070211   

          1190      1191  1192  
0     0.651019 -1.138258   4.0  
1     0.113089 -0.893152   3.0  
2    -1.587275 -0.142300   1.0  
3     1.002717 -0.438310   4.0  
4     1.800154 -0.814048   3.0  
...        ...       ...   ...  
8995  1.071156  0.724100   2.0  
8996 -1.323398 -0.551580   4.0  
8997 -0.348627 -0.028501   3.0  
8998 -0.937576 -1.578858   4.0  
8999  0.254605  0.259421   1.0  

[9000 rows x 1193 columns]
In [512]:
result_final_data.head()
Out[512]:
0 1 2 3 4 5 6 7 8 9 ... 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192
0 0.820282 3.720374 1.129934 -1.626893 -0.299511 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 1.013023 -0.589681 0.710931 0.651019 -1.138258 4.0
1 0.820282 -0.557090 -0.885007 0.208409 0.313454 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 0.0 1.744552 0.926937 -1.067492 0.113089 -0.893152 3.0
2 0.820282 -0.557090 1.129934 0.208409 -0.299511 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 2.968973 -0.419956 0.513002 -1.587275 -0.142300 1.0
3 0.820282 -0.454018 1.129934 0.208409 -0.299511 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.303877 -0.815112 0.252377 1.002717 -0.438310 4.0
4 0.820282 -0.196340 1.129934 2.043710 -0.299511 0.0 1.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.616000 -0.269426 0.987940 1.800154 -0.814048 3.0

5 rows × 1193 columns

In [ ]:
 
In [ ]:
 
In [ ]:
 
In [513]:
result_final_data_X = result_final_data.drop(1192, axis=1).copy()
result_final_data_y = result_final_data[1192]
In [526]:
from sklearn.model_selection import train_test_split
result_final_dataX_train, result_final_dataX_val, result_final_datay_train, result_final_datay_val = train_test_split(result_final_data_X, result_final_data_y, test_size=0.10, random_state=2022,stratify=result_final_data_y)
In [527]:
# import numpy as np

# result_final_data_X_array = result_final_data_X.values
In [528]:
# result_final_data_y_array = result_final_data_y.values
In [ ]:
 
In [532]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization
from tensorflow.keras import regularizers

model_dl_mlp_4 = Sequential([
    Input(shape=(result_final_dataX_train.shape[1],)),
    Dense(1024, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dropout(0.3),
    Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.003)),
    BatchNormalization(),
    Dense(5, activation='softmax')
])
In [536]:
# from tensorflow.keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
# from tensorflow.keras.utils import plot_model
from keras.utils import plot_model
# plot_model(model3, to_file='model_plot4.png', show_shapes=True, show_laye
plot_model(model_dl_mlp_4, to_file='model_dl_mlp_4.png',
 show_shapes=True,
 show_layer_names=True,
 layer_range=None,
 show_layer_activations=True,
 show_trainable=True)
Out[536]:
In [537]:
model_dl_mlp_4.summary()
Model: "sequential_22"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_134 (Dense)                    │ (None, 1024)                │       1,221,632 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_84               │ (None, 1024)                │           4,096 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_103 (Dropout)                │ (None, 1024)                │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_135 (Dense)                    │ (None, 512)                 │         524,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_85               │ (None, 512)                 │           2,048 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_104 (Dropout)                │ (None, 512)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_136 (Dense)                    │ (None, 256)                 │         131,328 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_86               │ (None, 256)                 │           1,024 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_105 (Dropout)                │ (None, 256)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_137 (Dense)                    │ (None, 128)                 │          32,896 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_87               │ (None, 128)                 │             512 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_106 (Dropout)                │ (None, 128)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_138 (Dense)                    │ (None, 64)                  │           8,256 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_88               │ (None, 64)                  │             256 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_107 (Dropout)                │ (None, 64)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_139 (Dense)                    │ (None, 32)                  │           2,080 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_89               │ (None, 32)                  │             128 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_140 (Dense)                    │ (None, 5)                   │             165 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
 Total params: 5,779,601 (22.05 MB)
 Trainable params: 1,925,189 (7.34 MB)
 Non-trainable params: 4,032 (15.75 KB)
 Optimizer params: 3,850,380 (14.69 MB)
In [533]:
model_dl_mlp_4.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
In [534]:
from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

history_model_mlp4 = model_dl_mlp_4.fit(result_final_dataX_train, result_final_datay_train, epochs=50, validation_split=0.2, batch_size=64, callbacks=[early_stopping])
Epoch 1/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 11s 45ms/step - accuracy: 0.2474 - loss: 8.4722 - val_accuracy: 0.2790 - val_loss: 7.5343
Epoch 2/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.3105 - loss: 7.4171 - val_accuracy: 0.3142 - val_loss: 6.7200
Epoch 3/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.3254 - loss: 6.5251 - val_accuracy: 0.3605 - val_loss: 5.8606
Epoch 4/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.3977 - loss: 5.6452 - val_accuracy: 0.3481 - val_loss: 5.1123
Epoch 5/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.4130 - loss: 4.8705 - val_accuracy: 0.3716 - val_loss: 4.4691
Epoch 6/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.4628 - loss: 4.2167 - val_accuracy: 0.3728 - val_loss: 3.9810
Epoch 7/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.5015 - loss: 3.6573 - val_accuracy: 0.3889 - val_loss: 3.5807
Epoch 8/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.5444 - loss: 3.2121 - val_accuracy: 0.4006 - val_loss: 3.2764
Epoch 9/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 36ms/step - accuracy: 0.5857 - loss: 2.8331 - val_accuracy: 0.3778 - val_loss: 3.1503
Epoch 10/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.6125 - loss: 2.5850 - val_accuracy: 0.3722 - val_loss: 3.0350
Epoch 11/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 39ms/step - accuracy: 0.6469 - loss: 2.3746 - val_accuracy: 0.3506 - val_loss: 2.9993
Epoch 12/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.6535 - loss: 2.2685 - val_accuracy: 0.3679 - val_loss: 2.9741
Epoch 13/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.7005 - loss: 2.1463 - val_accuracy: 0.3864 - val_loss: 2.9244
Epoch 14/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.7250 - loss: 2.0307 - val_accuracy: 0.3827 - val_loss: 2.9263
Epoch 15/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 36ms/step - accuracy: 0.7371 - loss: 2.0019 - val_accuracy: 0.3864 - val_loss: 2.9368
Epoch 16/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 38ms/step - accuracy: 0.7580 - loss: 1.9289 - val_accuracy: 0.3741 - val_loss: 3.0698
Epoch 17/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.7570 - loss: 1.9125 - val_accuracy: 0.3796 - val_loss: 3.0078
Epoch 18/50
102/102 ━━━━━━━━━━━━━━━━━━━━ 4s 37ms/step - accuracy: 0.7904 - loss: 1.8507 - val_accuracy: 0.3895 - val_loss: 2.9358
In [535]:
import matplotlib.pyplot as plt

plt.figure(figsize=(20, 6))

# Plot the first subplot (loss)
plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
plt.plot(history_model_mlp4.history["loss"])
plt.plot(history_model_mlp4.history["val_loss"])
plt.title("Validation Loss & Training Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
leg = plt.legend(["Training Loss", "Validation Loss"], loc="upper right")

# Plot the second subplot (accuracy)
plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot
plt.plot(history_model_mlp4.history["accuracy"])
plt.plot(history_model_mlp4.history["val_accuracy"])
plt.title("Validation Accuracy & Training Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
leg = plt.legend(["Training Accuracy", "Validation Accuracy"], loc="lower right")

plt.show()
In [539]:
y_pred_val_model_mlp4 = model_dl_mlp_4.predict(result_final_dataX_val)
29/29 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step
In [542]:
predicted_class_model_mlp4 = np.argmax(y_pred_val_model_mlp4, axis=1)
predicted_class_model_mlp4.shape
Out[542]:
(900,)
In [541]:
from sklearn.metrics import cohen_kappa_score

kp_model_mlp4=cohen_kappa_score(result_final_datay_val,predicted_class_model_mlp4)
kp_model_mlp4
Out[541]:
0.1875805825369964
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]: